面向对象的SPOT5图像森林分类研究
|
摘要
遥感图像森林分类是遥感技术在森林资源监测应用中最基础、最重要和最关键的工作。几十年来,遥感图像分类无论在理论上,还是在技术上都取得了长足的进步,但仍不能满足大面积实际应用需要,尤其是在森林分布破碎、类型多样、结构复杂的南方林区,遥感图像分类精度还有待于进一步提高。近年来,随着高空间分辨率航天遥感图像的容易获取,遥感技术在森林资源监测中应用前景十分良好。面向对象分析方法的提出和发展,为遥感图像森林分类提供了一个极有希望的途径。然而,现有的遥感理论和技术大多是基于中低分辨率图像、针对像元分析建立起来的,对于高空间分辨率图像、对于具有丰富含义的图像对象,需要从图像处理到信息提取、分类等各个方面都进行深入的研究。本文以改善SPOT5图像森林分类精度为目的,从图像预处理、图像分割、ETM+辅助数据的应用、对象的光谱和纹理特征提取与筛选、多分类器分类与结合等整个流程进行了全面的、系统的和综合的探索和研究,主要成果如下:
1)以采集高分辨率遥感几何精校正地面控制点(GCPs)和林业调查规划应用为目的,建立了林区GPS控制网。布网结果表明,基于该控制网的星站差分GPS单点定位精度RMS均小于0.5m,完全能够满足高空间分辨率的SPOT5、IKONOS和QuickBird等图像几何精校正GCPs采集的需要,解决了传统基于地形图选点和普通手持式GPS定位方法无法满足高分辨率遥感图像几何精校正精度要求的问题。在GPS控制网的支持下,普通手持式GPS的单点定位最大误差为3.86m,小班边界平均位移为3.23m,小班中心绝对位移平均为3.76m,小班面积测量精度达到98%以上,完全能够满足林业生产实际应用需要,且操作简便,工作效率高,适用于大范围的森林资源调查。建立林区GPS控制网是在当前普遍缺乏高精度地面控制点的情况下,进行高空间分辨率遥感数据几何精校正的有效途径。
2)比较全面地探讨了森林资源监测用SPOT5图像预处理的技术方法。试验结果表明,IHS变换融合和双线性内插法重采样在空间信息、纹理信息和光谱信息保持方面,均具有良好的效果。分段线性拉伸和边缘增强处理,有利于突出森林信息,有助于提高图像解译的精度。
3)研究了SPOT5图像结合Landsat ETM+图像进行图像分割和对象特征提取的策略,探讨了对象特征的筛选方法。ETM+图像由于空间分辨率相对较低,不能实质性地参与图像分割过程,否则会造成图像对象同质性差、“杂质”多,边界不准确,不能准确地反映地物空间分布状况。在人工林区,光谱信息和纹理信息是森林分类最有价值的信息。采用协方差、简单相关性和多重相关性进行对象特征筛选后,可剔除大部分对象特征,并保持对象特征原有含义不变、相互独立和具有一定的信息量,为后续分类器应用提供了可靠保证。
4)提出了图像分割—基于规则的分类—基于分类的分割—分区控制—底层分类—上层综合的高空间分辨率遥感图像(SPOT5图像)森林分类策略。试验了最小距离法、马氏距离法、Bayes准则、模糊分类法和支持向量机5个分类器,结果表明,Bayes分类器的总体精度最高,且各类型的生产者精度亦保持较为接近,对以龄组为基础、包含22个类型的第三级分类的总体分类精度达到了79.38%,以树种为基础的、包含15个类型的第二级分类的总体精度达到了81.82%,以树种组为基础的、包含9个类型的第三级分类的总体精度达到了86.33%。在景观复杂地区的森林分层分类,由底层分类开始、逐级向上合并的方法,比由顶级分类开始、往下逐级分类的方法,效果更好。ETM+作为辅助数据,较大程度地提高了SPOT5图像的分类精度。
5)研究了多分类器结合方法,发展了多分类器结合的投票/模糊法,该方法综合了保守投票规则和模糊融合方法,通过结合5个分类器的研究表明,该方法与标准结合方法、模糊融合方法相比,效果更好。多分类器结合后,总体分类精度有一定的提高,但提高幅度不大。
The classification of forest cover is the most important and essential task in employing remote sensing to forest resources inventory and monitoring.Obvious advancement had been achieved in theory and technology during past decades,thought the classification can still not full met the need of practical application,for the classification accuracy need to be further improved in the southern China,where were with fragmentized distribution of forest,species and types diversity,and complex structure.With the high spatial resolution remote sensing data are available recent years,remote sensing can be expected to applicate wildely in forest resources monitoring,and object-oriented methodology also provides us a hope method to image analysis.However,the existing theory and technology of remote sensing had been built on low or medium resolution imagery and based on pixel,so it is important and urgent to keep on study to imagery process,information extract,imagery classification for the high or very high resolution imagery and meaning object.Aiming toward improve the classification accuracy of SPOT5 imagery,a synthetical and systemic research was focused on the image processing,image segmentation,employment of Landsat 7 ETM+ image as ancillary,spectral and texture features extraction and selection,forest cover classification with multi-classifiers and combination,the main works and result are showed following:
1)To collect the ground control points(GCPs)for the geometric correction of the hi-spatial resolution remote sensing imagery and employment in forest resources inventory and planning,a GPS control network was found in the study area.The test results indicated that,the RMS error of single plot of GCPs positioned by WADGPS was lower than 0.5 m, which completely met the demands of geometric correction of the high-spatial resolution remote sensing image,such as SPOT5,QuickBird and IKONOS.Using GPS control network as the reference station and solving the high-precision coordinate conversion parameters,the largest RMS error of single plot positioned by portable GPS was 3.86 m.The average boundary displacement and absolute displacement of sub-compartment's center were 3.23 m and 3.76 m respectively and the accuracy of area survey was higher than 98%,which full satisfy the practice implication.The accuracy and efficiency of the application of GPS in forest district was improved effectively after establishing the GPS control network.
2)SPOT5 data processing methods for forest resources inventory in south china were studied.It was indicated that fusing data using IHS transformations and resampling by bilinear interpolation took much advantages in preserving spatial,texture and spectral information of image than any other methods,linear stretching by segment and edge enhancement were helpful to reveal forest information and improve classification accuracy.
3)The strategy of imagery segmentation and information extraction combining SPOT5 HRG imagery and Landsat 7 ETM+ imagery was studied,and the selection method of object features was discussed.ETM+ data couldn't take part in the imagery segmentation,for their coarser resolution would reduce the homogeneity within objects,lead to the objects contained impurities and couldn't represent the distribution of forest cover exactly.Spectrum and texture were most useful object features for the classification of forest cover in plantation.Most object features had been eliminated after selected by covariance,sample correlative coefficient and multiple correlative coefficient,the features remained were with the specific meaning,independence with each other,and had definite information.
4)A five-step object-oriented classification routine was present to do classification on SPOT5 imagery,it included imagery segmenting,rule-based classifying,classification-based segmenting,area control,multi-classifier classifying and combining,topper layer synthesizing. Five classifier were employed to the classification,included minimum distance classifier, Mahalanobis distance classifier,Bayes rule classifier,fuzzy classifier and support vector machines.The result of classification indicated that in the study area with the fragmentized distribution forest,species and type diversity,complex structure,with the Bayes classifier,the total accuracies of the third level,the second level and the first level were 79.38%,81.82% and 86.98%respectively,where the third level contained twenty-two class based on age-group of trees,the second level contained fifteen class based on species,and the first level included nine class based on species groups.Bayes rule classifier also got the closely producer accuracies of all classes.With hierarchical classification,the result of beginning from the low layer and then synthesizing upward topper-layer was better than that classifying from top-layer to low layers.Used as ancillary data,Landsat 7 ETM+ data were helpful to improve the classification accuracy of SPOT5 imagery.
5)The combination methods of multi-classifier were studied,a new combination method, defined as voting rule/fuzzy fusion,had been developed,which combined the conservative voting rule and fuzzy fusion.It got the better result than voting rule and fuzzy fusion through the test of combining five classifiers.The accuracy had been improved by combining classifiers,although the improvement was not as obvious as expected previously.
1)以采集高分辨率遥感几何精校正地面控制点(GCPs)和林业调查规划应用为目的,建立了林区GPS控制网。布网结果表明,基于该控制网的星站差分GPS单点定位精度RMS均小于0.5m,完全能够满足高空间分辨率的SPOT5、IKONOS和QuickBird等图像几何精校正GCPs采集的需要,解决了传统基于地形图选点和普通手持式GPS定位方法无法满足高分辨率遥感图像几何精校正精度要求的问题。在GPS控制网的支持下,普通手持式GPS的单点定位最大误差为3.86m,小班边界平均位移为3.23m,小班中心绝对位移平均为3.76m,小班面积测量精度达到98%以上,完全能够满足林业生产实际应用需要,且操作简便,工作效率高,适用于大范围的森林资源调查。建立林区GPS控制网是在当前普遍缺乏高精度地面控制点的情况下,进行高空间分辨率遥感数据几何精校正的有效途径。
2)比较全面地探讨了森林资源监测用SPOT5图像预处理的技术方法。试验结果表明,IHS变换融合和双线性内插法重采样在空间信息、纹理信息和光谱信息保持方面,均具有良好的效果。分段线性拉伸和边缘增强处理,有利于突出森林信息,有助于提高图像解译的精度。
3)研究了SPOT5图像结合Landsat ETM+图像进行图像分割和对象特征提取的策略,探讨了对象特征的筛选方法。ETM+图像由于空间分辨率相对较低,不能实质性地参与图像分割过程,否则会造成图像对象同质性差、“杂质”多,边界不准确,不能准确地反映地物空间分布状况。在人工林区,光谱信息和纹理信息是森林分类最有价值的信息。采用协方差、简单相关性和多重相关性进行对象特征筛选后,可剔除大部分对象特征,并保持对象特征原有含义不变、相互独立和具有一定的信息量,为后续分类器应用提供了可靠保证。
4)提出了图像分割—基于规则的分类—基于分类的分割—分区控制—底层分类—上层综合的高空间分辨率遥感图像(SPOT5图像)森林分类策略。试验了最小距离法、马氏距离法、Bayes准则、模糊分类法和支持向量机5个分类器,结果表明,Bayes分类器的总体精度最高,且各类型的生产者精度亦保持较为接近,对以龄组为基础、包含22个类型的第三级分类的总体分类精度达到了79.38%,以树种为基础的、包含15个类型的第二级分类的总体精度达到了81.82%,以树种组为基础的、包含9个类型的第三级分类的总体精度达到了86.33%。在景观复杂地区的森林分层分类,由底层分类开始、逐级向上合并的方法,比由顶级分类开始、往下逐级分类的方法,效果更好。ETM+作为辅助数据,较大程度地提高了SPOT5图像的分类精度。
5)研究了多分类器结合方法,发展了多分类器结合的投票/模糊法,该方法综合了保守投票规则和模糊融合方法,通过结合5个分类器的研究表明,该方法与标准结合方法、模糊融合方法相比,效果更好。多分类器结合后,总体分类精度有一定的提高,但提高幅度不大。
The classification of forest cover is the most important and essential task in employing remote sensing to forest resources inventory and monitoring.Obvious advancement had been achieved in theory and technology during past decades,thought the classification can still not full met the need of practical application,for the classification accuracy need to be further improved in the southern China,where were with fragmentized distribution of forest,species and types diversity,and complex structure.With the high spatial resolution remote sensing data are available recent years,remote sensing can be expected to applicate wildely in forest resources monitoring,and object-oriented methodology also provides us a hope method to image analysis.However,the existing theory and technology of remote sensing had been built on low or medium resolution imagery and based on pixel,so it is important and urgent to keep on study to imagery process,information extract,imagery classification for the high or very high resolution imagery and meaning object.Aiming toward improve the classification accuracy of SPOT5 imagery,a synthetical and systemic research was focused on the image processing,image segmentation,employment of Landsat 7 ETM+ image as ancillary,spectral and texture features extraction and selection,forest cover classification with multi-classifiers and combination,the main works and result are showed following:
1)To collect the ground control points(GCPs)for the geometric correction of the hi-spatial resolution remote sensing imagery and employment in forest resources inventory and planning,a GPS control network was found in the study area.The test results indicated that,the RMS error of single plot of GCPs positioned by WADGPS was lower than 0.5 m, which completely met the demands of geometric correction of the high-spatial resolution remote sensing image,such as SPOT5,QuickBird and IKONOS.Using GPS control network as the reference station and solving the high-precision coordinate conversion parameters,the largest RMS error of single plot positioned by portable GPS was 3.86 m.The average boundary displacement and absolute displacement of sub-compartment's center were 3.23 m and 3.76 m respectively and the accuracy of area survey was higher than 98%,which full satisfy the practice implication.The accuracy and efficiency of the application of GPS in forest district was improved effectively after establishing the GPS control network.
2)SPOT5 data processing methods for forest resources inventory in south china were studied.It was indicated that fusing data using IHS transformations and resampling by bilinear interpolation took much advantages in preserving spatial,texture and spectral information of image than any other methods,linear stretching by segment and edge enhancement were helpful to reveal forest information and improve classification accuracy.
3)The strategy of imagery segmentation and information extraction combining SPOT5 HRG imagery and Landsat 7 ETM+ imagery was studied,and the selection method of object features was discussed.ETM+ data couldn't take part in the imagery segmentation,for their coarser resolution would reduce the homogeneity within objects,lead to the objects contained impurities and couldn't represent the distribution of forest cover exactly.Spectrum and texture were most useful object features for the classification of forest cover in plantation.Most object features had been eliminated after selected by covariance,sample correlative coefficient and multiple correlative coefficient,the features remained were with the specific meaning,independence with each other,and had definite information.
4)A five-step object-oriented classification routine was present to do classification on SPOT5 imagery,it included imagery segmenting,rule-based classifying,classification-based segmenting,area control,multi-classifier classifying and combining,topper layer synthesizing. Five classifier were employed to the classification,included minimum distance classifier, Mahalanobis distance classifier,Bayes rule classifier,fuzzy classifier and support vector machines.The result of classification indicated that in the study area with the fragmentized distribution forest,species and type diversity,complex structure,with the Bayes classifier,the total accuracies of the third level,the second level and the first level were 79.38%,81.82% and 86.98%respectively,where the third level contained twenty-two class based on age-group of trees,the second level contained fifteen class based on species,and the first level included nine class based on species groups.Bayes rule classifier also got the closely producer accuracies of all classes.With hierarchical classification,the result of beginning from the low layer and then synthesizing upward topper-layer was better than that classifying from top-layer to low layers.Used as ancillary data,Landsat 7 ETM+ data were helpful to improve the classification accuracy of SPOT5 imagery.
5)The combination methods of multi-classifier were studied,a new combination method, defined as voting rule/fuzzy fusion,had been developed,which combined the conservative voting rule and fuzzy fusion.It got the better result than voting rule and fuzzy fusion through the test of combining five classifiers.The accuracy had been improved by combining classifiers,although the improvement was not as obvious as expected previously.
引文
[1]柏延臣,王劲峰.2005.结合多分类器的遥感专题数据专题分类方法研究.遥感学报,9(5):555-563
[2]包盈智,赵宪文等.1996.卫星数据用于森林资源二类调查的效益评估和分析.林业资源管理,(5):67-70
[3]曹宝,秦其明,马海建,等.2006.面向对象方法在 SPOT5遥感图像分类中的应用-以北京市海淀区为例.地理与地理信息科学,22(2):46-49、54
[4]陈晋,陈云浩,何春阳,等.2001.基于土地覆盖分类的植被覆盖率估算亚像元模型与应用.遥感学报,5(6):416-422
[5]陈云浩,冯通,史培军,等.2006.基于面向和规则的遥感影像分类研究.武汉大学学报(信息科学版),31(4):216-320
[6]陈忠,赵忠明.2005.基于区域生长的多尺度遥感图像分割算法.计算机工程与应用,35:7-9
[7]戴昌达,姜小光,唐伶俐.2004.遥感图像应用处理与分析.北京:清华大学出版社
[8]董得红.1996.TM 卫星影像在荒漠化土地普查与监测中的应用.林业资源管理,(6):72-74
[9]杜凤兰,田庆久,夏学齐,等.2004.面向对象的地物分类法分析与评价.遥感技术与应用,19(1):20-23
[10]冯仲科,余新晓.1999a.“3S”技术及其应用.北京:中国林业出版社
[11]冯仲科.1999b.现代林业测绘技术系统研究.北京测绘,(2):28-30
[12]冯仲科,游先祥,任谊群.2001a.基于3S 的森林资源与环境监测系统构想.北京林业大学学报,23(4):90-92
[13]冯仲科,游先祥.2001b.普通手持式 GPS 用于林区近实时差分定位的研究.林业科学,36(6):116-118
[14]冯仲科,邓融.2002a.基于手持式 GPS 接收机的林区坐标差分研究.森林工程,(2):13-14
[15]冯仲科,孟宪宇,韩熙春.2002b.建立我国多级分辨率的森林资源调查技术体系.北京林业大学学报,24(5/6):156-159
[16]甘淑,袁希平,何大明.2003.遥感专家分类系统在滇西北植被信息提取中的应用试验研究.云南大学学报(自然科学版),25(6):553-557
[17]高成发.1999.GPS 测量.北京:人民交通出版社
[18]郭峰,舒宁.2007.基于灰度-基元共生矩阵的多光谱遥感影像分类.测绘信息与工程,32(6):20-22
[19]贾世楼.2001.信息论理论基础.哈尔滨:哈尔滨工业大学出版社
[20]贾永红,李德仁.200t.多源遥感影像像素级融合分类与决策级分类融合法的研究.武汉大学学报信息科学版,(5):430-434
[21]瞿继双;徐德坤;王超.2004.基于结构上下文的模糊神经网络自动目标检测方法.中国图象图形学报(A 辑),19(10):1169-1174
[22]寇文正,肖燕,张瑞玺.1990.应遥感技术进行森林资源监测方法的研究.环境遥感,5(2):102-109
[23]李弼程,魏俊,彭天强.2004.遥感影像融合效果的客观分析与评价.计算机工程与科学,26(1):42-46
[24]李朝峰,王佳梁.2001.模糊控制 BP 网络的遥感图像分类方法研究.中国矿业大学学报(自然科学版),30(3):311-314
[25]李崇贵,赵宪文,斯林等.2001.用岭估计研究以 RS 和 GIS 为基础的森林郁闭度估测.林业科学,37(5).
[26]李崇贵,李春干.2004a.森林资源监测 Spot5遥感图像几何精校正方法研究.北京林业大学学报,(2):22-25
[27]李崇贵,赵宪文.2004b.林区地形起伏对 SPOT5遥感图像几何精校正的影响.北京林业大学学报,26(2):6-10
[28]李崇贵,李春干.2005a.森林资源调查林区 GPS 控制网的试验研究.林业科学,41(1):19-24
[29]李崇贵,赵宪文,李春干.2005b.森林蓄积量遥感估测理论与实现.北京:科学出版社
[30]李崇贵,陈华安,李春干.2005c.星源通掌上森林调查仪面积测量精度研究.林业科学,41(3):174-178
[31]李春干.2002.小班定位误差评价方法探讨.林业调查规划,27(4):16-17
[32]李春干,谭必增.2004.基于 GIS 的森林资源遥感调查方法研究.林业科学,40(4):40-45
[33]李春干,代华兵,李崇贵.2006.基于高分辨率卫星图像的小班勾绘精度检验.福建林学院学报,26(2):127-130
[34]李后强,王宜主,刘政凯.1997.一种适用于多类别遥感图像分类的新方法-复合神经网络分类方法.遥感学报,1(4):257-261
[35]李留瑜.1999.林业调查技术的回顾与思考.林业资源管理,(6):50-57
[36]李世玲,张富堂.2006.一种新的回归变量选择方法.计算机应用与软件,23(5):113-115
[37]李爽,丁圣彦,钱乐祥.2002.决策树分类法及其在土地覆盖分类中的应用.遥感技术与应用,17(1):6-11
[38]李小文.2005.定量遥感的发展与创新.河南大学学报(自然科学版),35(4):49-56
[39]李志林,朱庆.2003.数字高程模型.武汉:武汉大学出版社
[40]林辉.2005.基于多特征的高分辨率遥感图像分类技术研究.长沙:中南林学院博士学位论文
[41]刘亮,姜小光,李显彬,等.2006.利用高光谱遥感数据进行农作物分类方法研究.中国科学院研究生院学报,23(4):484-488
[42]刘基余,李征航,王跃虎,等.2003.全球卫星定位及其应用.北京:科学出版
[43]刘顺喜,张定祥,尤淑撑,等.2005.分区控制和分层提取下的 OMIS-I 影像土地利用/覆盖分类方法研究.地理与地理信息科学,21(5):28-32
[44]刘晓龙.2002.基于影像匹配接边纠正的数字正射影像的镶嵌技术.遥感学报,6(2):104-109
[45]刘旭升,张晓丽.2005.基于 BP 神经网络的森林植被遥感分类研究.林业资源管理,(1):51-54
[46]刘亚岚,阎守邕,王涛,等.2002.遥感图像分区自动分类方法研究.遥感学报,6(5):359-326.
[47]骆剑承,周成虎,杨艳.2000a.基于径向基函数(RBF),映射理论的遥感影像分类模型研究.中国图象图形学报,5(A 版)(2):94-99
[48]骆剑承,王钦敏,周成虎,等.2000b.基于自适应共振模型的遥感影像分类方法研究.测绘学报,31(2):145-150
[49]骆剑承,周成虎,梁怡,等.2002.支持向量机及其遥感影像空间特征提取和分类的应用研究.遥感学报,6(1):50-55
[50]明冬萍,骆剑承,周成虎,等.2005.高分辨率遥感影像信息提取及块状基元特征提取.数据采集与处理,20(1):34-39
[51]牛明香,赵庚星,李尊英.2004.南四湖湿地遥感信息分区分层提取研究.地理与地理信息科学.20(2):45-52.
[52]欧阳鑫玉,赵楠楠.2002.图像分割技术的发展.鞍山钢铁学院学报,25(5):363-368
[53]乔伟峰,孙在宏,后斌.2003.利用 SPOT5数据制作正射影像图并辅助更新土地利用现状图的方法.南京师范大学学报(自然科学版),26(3):112-116
[54]任青山,杨小林,范文义.2001.3S 技术在拉萨市森林植被调查中的应用.东北林业大学学报,29(4):18-21
[55]沙占江,马海州,李玲琴,等.2004.多尺度空间分层聚类算法在土地利用与土地覆被研究中的应用.地理科学,24(8):477-483
[56]师庆东,吕光辉,潘晓玲,等.2003.遥感影像中分区分类法及在新疆北部植被分类中的应用.干旱区地理,26(3):264-268.
[57]施益强,陈崇成,陈玲.2004.遥感技术在环境资源中的应用进展与展望,国土资源遥感,(4):7-13
[58]孙丹峰,汲长远,林培.1999.自组织网络在遥感土地覆盖分类中应用研究.遥感学报,3(2):139-143
[59]唐守正,陈永富.2006.关于森林资源监测体系改革的一些思考.见:森林可持续经营探索与实践.北京:中国林业出版社,27-32
[60]唐新明,林宗坚,吴岚.1999.基于等高线和高程点建立 DEM 的精度评价方法探讨.遥感信息,(3):7-10
[61]王海晖,彭嘉雄,吴巍,等.2003.多源遥感图像融合效果评价方法研究.计算机工程与应用,25(2):33-37
[62]王惠文.1999.偏最小二乘回归方法及其应用.北京:国防工业出版社
[63]王家耀,崔铁军,苗国强.2004.数字高程模型及其数据结构.海洋测绘,24(3):1-4
[64]王建武,肖红生,张利民.2000.GPS 在沿海围垦动态监测中的应用.华南农业大学学报,21(2):1-4.
[65]王任华,霍宏涛,游先祥.2003.人工神经网络在遥感图像森林植被分类中的应用.北京林业大学学报,25(4):1-5
[66]王松桂,陈敏,陈立萍.1999.线性统计模型线性回归与方差分析.北京:高等教育出版社
[67]王圆圆,李京.2004.遥感影像土地利用/覆盖分类方法研究综述.遥感信息,(1):53-59
[68]卫建军,李新平,赵东波,等.2006.混合像元分离的研究进展.水土保持研究,13(5):103-105
[69]翁永玲,田庆久.2003.遥感数据融合方法分析与评价综述.遥感信息,3:49-54
[70]熊轶群,吴健平.2006.面向对象的城市绿地信息提取方法研究.华东师范大学学报(自然科学版),(4):84-90
[71]徐冠华.1994.三北防护林地区再生资源遥感的理论及其技术应用.北京:中国林业出版社.
[72]徐天蜀,张王菲,岳彩荣.2007.基于 PCA 的森林生物量遥感信息模型研究.生态环境,16(6):1759-1762
[73]杨长保,丁继红.2006.面向对象的遥感图像分类方法研究.吉林大学学报(地球科学版),36(4):642-646
[74]杨丽英,覃征,张选平.2005.分类器模拟算法及其应用.西安交通大学学报,39(12):1311-1314.
[75]游先祥,李福林,王化昌,等.1995.森林资源调查、动态监测、信息管理.北京:中国林业出版社.
[76]游先祥主编.2003.遥感原理及其在资源环境中的应用.北京:中国林业出版社
[77]曾志远.2004.卫星遥感图像计算机分类与地学应用研究.北京:科学出版社
[78]张锦水,何春阳,潘耀忠,等.2006.基于 SVM 的多源信息复合的高空间分辨率遥感数据分类研究.遥感学报,10(1):49-57
[79]张路,廖明生.2006.一种顾及上下文的遥感影像模糊聚类.遥感学报,10(1):58-65
[80]张秀英,冯学智,刘伟.2007.基于多分类器结合的 IKONOS 影像城市植被类型识别.东南大学学报(自然科学版),37(3):399-403
[81]张翊涛,陈洋,王润生.2005.结合自动分区与分层分析的多光谱遥感图像地物分类方法.遥感技术与应用,20(3):332-337
[82]赵宪文,李崇贵,斯林,等.2001a.森林资源遥感估测的重要进展.中国工程科学,3(8):15-28
[83]赵宪文,李崇贵.2001b.基于“3S”的森林资源定量估测-原理、方法、应用及软件实现.北京:中国科学技术出版社
[84]赵宪文,李崇贵,斯林,等.2002.基于信息技术的森林资源调查新体系.北京林业大学学报,24(5/6):147-155
[85]赵英时等.2003.遥感应用分析原理与方法.北京:科学出版社.
[86]周前祥,敬忠良,姜世忠 2003.不同光谱与空间分辨率遥感图像融合方法的理论研究.遥感技术与应用,18(1):41-46
[87]周忠谟,易杰军,周琪.1992.GPS 卫星测量原理与应用.北京:测绘出版社
[88]朱述龙,张占睦.2000.遥感图像获取与分析.北京:科学出版社
[89]朱维凡.1999.TM 卫星图象在二类调查应用中的探讨.林业资源管理,(3):54-56
[90]ADAMS,J.B.,SMITH,M.O.and GILLESPIE,A.R.,1993,Imagine spectroscopy:interpretation based on spectral mixture analysis.In C.M.Pieters and P.A-J.Englert(Eds),Remote Geochemical Analysis:Elemental and mineralogical composition,pp.145-166(New York:Cambridge University Press).
[91]ADAMS,J.B.,SABOL,D.E.;KAPOS,V.,FILHO,R.A.,et al.,1995,Classification of multispectral images based on fractions of endmembers:application to land cover change in the Brazilian Amazon.Remote Sensing of Environment,52.pp.137-154.
[92]ARGIALAS,D.,TZOTSOS,A.2006,Automatic extraction of physiographic features and alluvial fans in Nevada,USA fiom digial elevation models and sattlite imagery through multiresolution segmentation and object-oriented classification.ASPRS 2006 Annual Conference,Prospecting for Geospatial Information Integration.1-10
[93]APLIN,P.,ATKINSON,P.M.and CURRAN,P.J.,1999a,Per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors:problems and potential solutions.In P.M.Atkinson and N.J.Tate(Eds),Advances in Remote Sensing and GISAnalysis,pp.219-239(New York:John Wiley and Sons).
[94]APLIN,P.,ATKINSON,P.M.and CURRAN,P.J.,1999b,Fine spatial resolution sedated satellite sensor imagery for land cover mapping in the United Kingdom.Remote Sensing of Environment,68.pp.206-216.
[95]APLIN,P.and ATKINSON,P.M.,2001,Sub-pixel land cover mapping for per-field classification.International Journal of Remote Sensing,22,pp.2853-2858.
[96]ASNER,G.P,and HEIDEBRECHT,K.B.,2002.Spectral unmixing of vegetation,soil and dry carbon cover in arid regions:comparing multispectral and hyperspectral observations.International Journal of Remote Sensing,23,pp.3939-3958.
[97]ATKINSON,P.M.and CURRAN,P.J.,1997.Choosing an appropriate spatial resolution for remote sensing investigations.Photogrammetric Engineering and Remote Sensing,63,PP.1345-1351.
[98]BAATZ,M.,SCH(A|¨)PE,A.,2000,Multiresolution Segmentation-an optimization approach for high quality multi-scale image segmentation;Proceedings:Angewandte Geographische Informations verarbeitung(AGIT)Ⅻ,Salzburg,Austria,Wichmann Verlag,pp12-23
[99]BATESON,A.and CURTISS,B.,1996,A method for manual endmember selection and spectral unrnixing.Remote Sensing of Environment,55,pp.229-243.
[100]BENEDIKTSSON,J.A.and KANELLOPOVLOS,I.,1999,Classification of multisource and hyperspectral data based on decision fusion.IEEE Transactions on Geoscience andRemote Sensing,37,pp.1367-1377.
[101]BENZ,U.C.,HOFMANN,P.,WILLHAUCK,G.,LINGENFELDER,I.,et al.,2004,Multi-resolution object-oriented fuzzy analysis of remote sensing data for GIS-ready information.ISPRS Journal of Photogrammetry & Remote Sensing.58,pp.239-258.
[102]BINAGHI,E.,MADELLA,P.,MONTESANO,M.G.and RAMPINI,A.,1997,Fuzzy contextual classification of multisource remote sensing images.IEEE Transactions on Geoscience and Remote Sensing,35,pp.326-339.
[103]BINAGHI,E.,BRIVIO,P.A.,GHEZZI,P.and RAMPINI,A.,1999,A fuzzy set accuracy assessment of soft classification.Pattern Recognition Letters,20,pp.935-948.
[104]BLOCH,I.,1996,Information combination operators for data fusion:a comparative review with classification.IEEE Transactions on Systems,Man,and Cybernetics,26,pp.52-67.
[105]CIHLAR,J.,2000,Land cover mapping of large areas fiom satellites:status and research priorities.International Journal of Remote Sensing,21,pp.1093-1114.
[106]COPPIN,P.;JONCKHEERE,I..,NACKAERTS.K.,MUYS,B.,et al.,2004,Digital change detection methods in ecosystem monitoring:a review.International Journal of Remote Sensing,25(9):1565-1596
[107]CORTIJO,F.J.and DE LA BUNCA,N.P.,1998,Improving classical contextual classification.International Journal of Remote Sensing,19,pp.1591-1613.
[108]CRACKNELL,A.P.,1998,Synergy in remote sensing—what's in a pixel? International Journal of Remote Sensing,19,pp.2025-2047.
[109]CUSHNIE,J.L.,1987,The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies.International Journal of Remote Sensing,8,pp.15-29.
[110]DEAN,A.M.and SMITH,G,M.,2003,An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities.International Journal of Remote Sensing,24,pp.2905-2920.
[111]DEFINIENS AG,2006a,Definiens professional 5 reference book.Ver.5.0.6.1.
[112]DEFINIENS AG,2006b,Definiens professional 7 User Guide.Ver.7.0.2.936
[113]DEFRIES,R.S.and CHAN,J.C.,2000,Multiple criteria for evaluating machine learning algorithms for land cover classification flora satellite data.Remote Sensing of Environment,74,pp.503-515.
[114]EKSTRAND,S.1996,Landsat TM-Based Forest Damage Assessment,Correction for Topographic Effects.Photogrammetric Engineering and Remote Sensing,62(2),pp.151-161.
[115]FAUVEL,M.,CHANUSSOT,J.,BENEDIKTSSON,J.A.,2006,Decision fusion for the classification of urban remote sensing images.IEEE Transactions on Geoscience and Remote Sensing,44(10),pp.2828-2838.
[116]FISHER,P.,1997,The pixel:a snare and a delusion.International Journal of Geographical Information System,7,pp.349-366.
[117]FLYGARE,A-M.,1997,A comparison of contextual classification methods using Landsat TM.International Journal of Remote Sensing,18,pp.3835-3842.
[118]FOODY,G.M.and COX,D.P.,1994,Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions.International Journal of Remote Sensing,15,pp.619-631.
[119]FOODY,G.M.,1996,Approaches for the production and evaluation of fuzzy land cover classification from remotely-sensed data.International Journal of Remote Sensing,17,pp.1317-1340.
[120]FOODY,G.M.and ARORA,M.K.,1997,An evaluation of some factors affecting the accuracy of classification by an artificial neural network.International Journal of Remote Sensing,18,pp.799-810.
[121]FOODY,G.M.,1999,Image classification with a neural network:from completely-crisp to fully-fuzzy situation.In P.M.Atkinson and N.J.Tate(Eds),Advances in Remote Sensing and GIS Analysis,pp.17-37(New York:John Wiley and Sons).
[122]FOODY,G.M.,2002,Status of land cover classification accuracy assessment.Remote Sensing of Environment,80,pp.185-201.
[123]FOODY,G.M.and MATHUR,A..2004,Arelative evaluation of multiclass image classification by support vector machines.IEEE Transactions on Geoscience and Remote Sensing,42,pp.1335-1343.
[124]FORD.G.E..and ZANELLI,C.I.,1985,Analysis and Quantification of Errors in the Geometric Correction of Satellite Images.Photogrammetric Engineering and Remote Sensing,51(11),pp.1725-1734.
[125]FRANKLIN,J.,PHINN,S.R.,WOODCOCK,C.E.and ROGAN,J.,2003,Rationale and conceptual framework for classification approaches to assess forest resources and properties.In M.A.Wulder and S.E.Franklin(Eds),Remote Sensing of Forest Environments Concepts and case studies,pp.279-300(Boston:Kluwer Academic Publishers).
[126]FRANKLIN,S.E.and WULDER.M.A.,2002,Remote sensing methods ill medium spatial resolution satellite data land cover classification of large areas.Progress In Physical Geography,26,pp.173-205.
[127]FRANKLIN,S.E.,PEDDLE,D.R..DECHKA,J.A.and STENHOUSE,G.B.,2002,Evidential reasoning with Landsat TM,DEM and GIS data for land cover classification in support of grizzly bear habitat mapping.International Journal of Remote Sensing,23,pp.4633-4652.
[128]FRIEDL,M.A.,BRODLEY,C.E.and STRAHLER,A.H.,1999,Maximizing land cover classification accuracies produced by decision trees at continental to global scales.IEEE Transactions on Geoscience and Remote Sensing,37,pp.969-977.
[129]FU,K.S.and MUI,J.K.1981,A survey of image segmentation.Pattern Recognition,13(1),pp.3-16
[130]GALLEGO,F.J.,2004,Remote sensing and land cover area estimation.International Journal of Remote Sensing,25,pp.3019-3047.
[131]GHOSH,J.,TUMER,K.,BECK,S.,et al.1995,Integration of Neural Classifiers for Passive Sonar Signals.Leondes DSP Theory and App lications.Academic Press.
[132]GITAS,I.Z.,MITRI,G.H.and VENTURA,G.,2004,Object-based image classification for burned area mapping of Creus Cape Spain,using NOAA-AVHRR imagery.Remote Sensing of Environment,92,pp.409-413.
[133]GONG,P.and HOWARTH,P.J.,1992,Frequency-based contextual classification and gray-level vector reduction for land-use identification.Photogrammetric Engineering and Remote Sensing,58,pp.423-437.
[134]GONG,P.,MARCEAU,D.J.and HOWARTH,P.J.,1992,A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data.Remote Sensing of Environment,40,pp.137-151.
[135]HARALICK,R.M.and SHAPIRO,L.G.,1985.Survey:Image Segmentation Techniques.Computer Vision,Graphics,and Image Processing,29(1):100-132.
[136]HARRIS,P.M.and VENTURA,S.J.,1995,The integration of geographic data with remotely sensed imagery to improve classification in an urban area.Photogrammetric Engineering and Remote Sensing,61,pp.993-998.
[137]HEROLD,M.,LIU,X.,and CLARKE,K.C.,2003,Spatial metrics and image texture for mapping urban land use.Photogrammetric Engineering and Remote Sensing,21,pp.2163-2183.
[138]HILL,J.,and STURM,B.,1991,Radiometric correction of multitemporal thematic mapper data for use in agricultural land-cover classification and vegetation monitoring.International Journal o f Remote Sensing,12(7):1471-1491
[139]HODGSON,M.E.,JENSEN,J.R.,TULLIS,J.A.,RJORDAN,K.D.,et al.,2003,Synergistic use lidar and color aerial photography for mapping urban parcel imperviousness.Photogrammetric Engineering and Remote Sensing,69,pp.973-980.
[140]HUANG,Z.and LEES,B.G.,2004.Combining non-parametric models for multisource predictive forest mapping.Photogrammetric Engineering and Remote Sensing,70,pp.415-425.
[141]HUANG,C,DAVIS,L.S.,and TOWNSHEND,J.R.G.,2002,An assessment of support vector machines for land cover classification.International Journal of Remote Sensing,23,pp.725-749.
[142]HUBERT-MOY,L.,COTONNEC,A.,LE DU,L.,CHARDIN,A.,et al.,2001,A comparison of parametric classification procedures of remotely sensed data applied on different landscape units.Remote Sensing of Environment,75.pp.174-187.
[143]HUGUENIN,R.L.,KARASKA,M.A.,BLARICOM,D.V.and JENSEN,J.R.,1997,Subpixel classification of Bald Cypress and Tupelo Gum trees in Thematic Mapper imagery.Photogrammetric Engineering and Remote Sensing,63,PP.717-725.
[144]IRONS,J.R.,MARKHAM.B.L.,NELSON,R.F.,TOLL,D.L.,et al.,1985,The effects of spatial resolution on the classification of Thematic Mapper data.International Journal of Remote Sensing,6,pp.1385-1403.
[145]JANSSEN,L.F.and MOLENAAR,M..1995,Terrain objects:their dynamics and their monitoring by integration of GIS and remote sensing.IEEE Transactions of Geoscience and Remote Sensing,33,pp.749-758.
[146]JANZ,K.,1993,World forest resources assessment 1990:An overview.Unasylva,44(174),pp.1-15
[147]KARTIKEYAN,B.,GOPALAKRISHNA,B.,KALUBARME,M.H.and MAIUMDER,K.L.,1994,Contextual techniques for classification of high and low resolution remote sensing data.International Journal of Remote Sensing,15.pp.1037-1051.
[148]KEUCHEL,J.,NAVMANN,S.,HEILER,M.and SIEGMUND,A.,2003,Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data.Remote Sensing of Environment,86,pp.530-541.
[149]KIM,H.,PANG,S.,JE,H.,KIM,D.,and BANG,S.Y.,2003,Constructing support vector machine ensemble.Pattern Recognition,36,pp.2757-2767.
[150]KOHL M.,2004,Forest Inventory and Monitoring.Encyclopedia afforest science,pp.403-409.
[151]KONTOES,C,WILKINSON,G.G.,BURRILL,A.,GOFFREDO,S.,et al.,1993,An experimental system for the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture.International Journal of Geographical Information Systems,7,pp.247-262.
[152]KOROTKOV,A.V.,PECK,T.J.,1993,Forest resources of the industrialized countries:An ECE/FAO assessment.Unasylva,44(174),pp.1-31.
[153]KULKARNI,A.D.and LULLA,K.,1999,Fuzzy neural network models for supervised classification: multispectral image analysis.Geocarto International.14,pp.42-50.;154]LAWRENCE,R.,BUNN,A.,POWELL,S.and ZMABON,M.,2004,Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis.Remote Sensing of Environment,90,pp.331-336.
[155]LIU,W.,GOPAL,S.and WOODCOOCK,C.E.,2004,Uncertainty and confidence in land cover classification using a hybrid classifier approach.Photogrammetric Engineering and Remote Sensing,70,pp.963-972.
[156]LLOYD,CD.,BERBEROGLU,S.,CURRAN,P.J.and ATKINSON,P.M.,2004,A comparison of texture measures for the per-field classification of Mediterranean land cover.International Journal of Remote Sensing,25,pp.3943-3965.
[157]LOBO,A.,CHIC,O.and CASTERAD,A.,1996,Classification of Mediterranean crops with multisensor data:per-pixel versus per-object statistics and image segmentation.International Journal of Remote Sensing,17,pp.2385-2400.
[158]LU,D.,MORAN,E.and BATISTELM,M.,2003,Linear mixture model applied to Amazonian vegetation classification.Remote Sensing of Environment,87,pp.456-469.
[159]LU,D.and WENG,Q.,2004,Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+ imagery.Photogrammetric Engineering and Remote Sensing,70,pp.1053-1062.
[160]LU D.,WENG Q.,2007,A survey of image classification methods and techniques of improving classification performance.International Journal of Remote Sensing,38(5-6),pp.823-870
[161]MAGUSSEN,S.,BOUDEWYN,P.and WULDER,M.,2004,Contextual classification of Landsat TM images to forest inventory cover types.International Journal of Remote Sensing,25,pp.2421-2440.
[162]MANNAN,B.and RAY,A.K.,2003,Crisp and fuzzy competitive learning networks for supervised classification of multispectral IRS scenes.International Journal of Remote Sensing,24,pp.3491-3502.
[163]MANNAN,B.,ROY,J.and RAY,A.K.,1998,Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images.International Journal of Remote Sensing,19,pp.767-774.
[164]MASELLI,F.,2001,Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analysis.Remote Sensing of Environment,75,pp.29-38.
[165]MICHAEL,A.W.,WERNER A.K.,MARK,G.,2004,National level forest monitoring and modeling in Canada.Progress in Planning,61 (May),pp.365-381
[166]MICHAEL,S.C,FRANKLIN,S.E.and MICHAEL,A.W.,2006,Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters.Photogrammetric Engineering & Remote Sensing,72(4),pp.383-394.
[167]MICHELSON,D.B.,LJLJEBERG,B.M.and PILESJO,P.,2000,Comparison of algorithms for classifying Swedish land cover using Landsat TM and ERS-1 SAR data.Remote Sensing of Environment,71,pp.1-15.
[168]MYINT,S.W.,2001,A robust texture analysis and classification approach for urban land-use and land-cover feature discrimination.Geocarto International,16,pp.27-38.
[169]NEVILLE,R.A.,LEVESQUE,J.,SUENE,K.,NADEAV,C,et ah,2003,Spectral unmixing of hyperspectral imagery for mineral exploration:comparison of results from SFSI and AVIRIS.Canadian Journal of Remote Sensing,29,pp.99-110.
[170]OKTN,G.S.,ROBERTS,D.A.,MURRAY,B.and OKIN,W.J.,2001,Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments.Remote Sensing of Environment,77,pp.212-225.
[171]OLTHOF,I.,KING,D.J.and LAUTENSCHLAGER,R.A.,2004,Mapping deciduous forest ice storm damage using Landsat and environmental data.Remote Sensing of Environment,89,pp.48-96.
[172]PAL,M.and MATHER,P.M.,2003,An assessment of the effectiveness of decision tree methods for land cover classification.Remote Sensing of Environment,86,pp.554-565.
[173]PAL,M.and MATHER,P.M.,2004,Assessment of the effectiveness of support vector machines for hyperspectral data.Future Generation Computer System,20,pp.1215-1225.
[174]PAL,N.R.and PAL,S.K.1993,A review on image segmentation techniques.Pattern recognition,26(9),pp.1277-1294
[175]PAOLA,J.D.and SCHOWENGERDT,R.A.,1995,A review and analysis of back propagation neural networks for classification of remotely sensed multispectral imagery.International Journal of Remote Sensing,16,pp.3033-3058.
[176]PEDDLE,D.R.and FERGUSON,D.T.,2002,Optimization of multisource data analysis:an example using evidential reasoning for GIS data classification.Computer & Geosciences,28,pp.45-52.
[177]PEDLEY,M.I.and CURRAN,P.I..1991,Per-field classification of an example using SPOT HRV imagery.International Journal of Remote Sensing,12,pp.2181-2192.
[178]PENALOZA,M.A.and WELCH,R.M.,1996,Feature selection for classification of polar regions using a fuzzy expert system.Remote Sensing of Environment,58,pp.81-100.
[179]PLATT,R.V.and GOETZ,A.F.H.,2004,A comparison of AVIRIS and Landsat for land use classification at the urban fringe.P hotogrammetric Engineering and Remote Sensing,70,pp.813-819.
[180]PRICE,K.P.,GUO,X.and STILES,J.M.,2002,Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas.International Journal of Remote Sensing,23,pp.5031-5042.
[181]REESE,H.,NILSSON,M.,PAHLEN,TG.,TINGELOF,U.,2003,Countrywide estimates of forest variables using satellite data and field data from the national forest inventory.Ambio,32(8),pp.542-548.
[182]RGIALAS,D.,TZOTSOS,A.,2006,Automatic extraction of physiographic feature and alluvial fans in Nevada,USA from digial elevation models and satellite imagery through multiresolution segmentation and object-oriented classification.ASPRS 2006 Annual Conference Reno,Nevada May 1-5.
[183]ROBERTS,D.A.,BATJSTA,G.T.,PEREIRA,J.L.G.,WALLER,E.K.,et al.,1998a,Change identification using multiemporal spectral mixture analysis:applications in eastern Amazonia.In R.S.Lunetta and C.D.Elvidge(Eds),Remote Sensing Change Detection:Environment Monitoring Methods and Applications,pp.137-161(Chelsea,MI:Ann Arbor Press).
[184]ROBERTS,D.A.,GARDNER,M.,CHURCH,R.,USTIN,S.,et al.,Ⅰ998b,Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models.Remote Sensing of Environment,65,pp.267-279.
[185]ROLI,F.,GIACINTO,G.,VEMAZZA,G.,1997,Comparison and combination of statistical and neural network alogrithms for remote sensing image classification.Kanellopoulos,Wilkinson G.,Roli,F.,et al.,Neurocomputation in Remote Sensing Data Anaylysis.Springer.
[186]SCHIEVE,J.,TUFTE,L.and Ehlers,M.,2001,Potential and problems of multi-scale segmentation methods in remote sensing.GeoBIT/GIS 6,pp.34-39.
[187]SCHOWENGERDT,R.A.,1996,On the estimation of spatial-spectral mixing with classifier likelihood functions.Pattern Recognition Letters,17,pp.1379-1387.
[188]SHALAN,M.A.,ARORA,M.K.and GHOSH,S.K.,2003,An evaluation of fuzzy classifications from IRS1C LISS Ⅲ imagery:a case study.International Journal of Remote Sensing,24,pp.3179-3186.
[189]SHARMA,K.M.S.and SARKAR,A.,1998,A modified contextual classification technique for remote sensing data.Photogrammetric Engineering and Remote Sensing,64,pp.273-280.
[190]Siev(a|¨)nen,T.2004.Recreation:Inventory,monitoring and management.In:Burley,J.,Evans,J.& Youngquist,J.A.(eds.).Encyclopedia offorest sciences.Elsevier,pp.958-964.
[191]SMALL,C.,2004,The Landsat ETM+spectral mixing space.Remote Sensing of Environment,93,pp.1-17.
[192]SMITH,W.B.,2002,Forest inventory and analysis:a national inventory and monitoring program.Environmental Pollution.116(suppl 1):S233-S242.
[193]SMITH,M.O.,USTIN,S.L.,ADAMS,J.B.and GILLESPIE,A.R.,1990,Vegetation in Deserts:Ⅰ.A regional measure of abundance flom multispectral images.Remote Sensing of Environment,31,pp.1-26.
[194]SOUTH,S.,QI,J.and LUSCH,D.P.,2004,Optimal classification methods for mapping agricultural tillage practices.Remote Sensing of Environment,91,pp.90-97.
[195]STEELE,B.M.,2000,Combining multiple classifiers:an application using spatial and remotely sensed information for land cover type mapping.Remote Sensing of Environment,74,pp.545-556.
[196]STUCKENS,J.,COPPIN,P.R.and BAUER,M.E.,2000,Integrating contextual information with per-pixel classification for improved land cover classification.Remote Sensing of Environment,71,pp.282-296.
[197]SUEN,C.Y.,LAM,L.,2000,Multiple classifier combination methodologies for differnent output levels.Kittler,Roli.Proceedings of First International Workshop on Multiple Classifier Systems,Springer.1857,pp.52-66.
[198]SUGUAMRAN.R.,ZERR,D.and PRATO,T.,2002,Improved urban land cover mapping using multitemporal IKONOS images for local government planning.Canadian Journal of Remote Sensing,28,pp.90-95.
[199]THOMAS,N.,HENDRIX.C.and CONGALTON,R.G.,2003,A comparison of urban mapping methods using high-resolution digital imagery.Photogrammetric Engineering and Remote Sensing,69,pp.963-972.
[200]VAGEN,T.-G.,2006,Remote sensing of complex land use change trajectories-a case study from the highlands of Madagascar.Agriculture,Ecosystems and Environment,115,pp.219-228.
[201]WANG,L.,SOUSA,W.P.,GONG,P.and BIGING,G.S.,2004,Comparison of IKONOS and Quick.Bird images for mapping mangrove species on the Caribbean coast of panama.Remote Sensing of Environment,91,pp.432-440.
[202]WOODCOCK,C.E.and GOPAL,S.,2000,Fuzzy set theory and thematic maps:accuracy assessment and area estimation.International Journal of Geographical Information Science,14,pp.153-172.
[203]XU,L.,KRZYZAK,A.,SUEN C,Y.,1992,Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition.IEEE Transaction on Systems,Man,and Cybernetics,22(3),pp.418-435.
[204]XU,B.,GONG,P.,SETO.E.and SPEAR,R.,2003,Comparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic IKONOS image.Photogrammetric Engineering and Remote Sensing,69,pp.529-536.
[205]YU,Q.,GONG,P.,CLINTON,N.,BIGIBG,G.et al.,2006,Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery.Photogrammetric Engineering and remote sensing,72(7),pp.799-811
[206]HUANG,X.,ENGEL,B.A.,XIONG,X.and JOHANNSEN,C.J.,1995,Analysis of classification results of remotely sensed data and evaluation of classification algorithms.Photogrammetric Engineering and Remote Sensing,61,pp.427-433.
[207]ZHANG,Y.,1994,Optimisation of building detection in satellite images by combining multispectral classification and texture filtering.ISPRS Journal of Photogrammetry andRemote Sensing,54,pp.50-60.
[2]包盈智,赵宪文等.1996.卫星数据用于森林资源二类调查的效益评估和分析.林业资源管理,(5):67-70
[3]曹宝,秦其明,马海建,等.2006.面向对象方法在 SPOT5遥感图像分类中的应用-以北京市海淀区为例.地理与地理信息科学,22(2):46-49、54
[4]陈晋,陈云浩,何春阳,等.2001.基于土地覆盖分类的植被覆盖率估算亚像元模型与应用.遥感学报,5(6):416-422
[5]陈云浩,冯通,史培军,等.2006.基于面向和规则的遥感影像分类研究.武汉大学学报(信息科学版),31(4):216-320
[6]陈忠,赵忠明.2005.基于区域生长的多尺度遥感图像分割算法.计算机工程与应用,35:7-9
[7]戴昌达,姜小光,唐伶俐.2004.遥感图像应用处理与分析.北京:清华大学出版社
[8]董得红.1996.TM 卫星影像在荒漠化土地普查与监测中的应用.林业资源管理,(6):72-74
[9]杜凤兰,田庆久,夏学齐,等.2004.面向对象的地物分类法分析与评价.遥感技术与应用,19(1):20-23
[10]冯仲科,余新晓.1999a.“3S”技术及其应用.北京:中国林业出版社
[11]冯仲科.1999b.现代林业测绘技术系统研究.北京测绘,(2):28-30
[12]冯仲科,游先祥,任谊群.2001a.基于3S 的森林资源与环境监测系统构想.北京林业大学学报,23(4):90-92
[13]冯仲科,游先祥.2001b.普通手持式 GPS 用于林区近实时差分定位的研究.林业科学,36(6):116-118
[14]冯仲科,邓融.2002a.基于手持式 GPS 接收机的林区坐标差分研究.森林工程,(2):13-14
[15]冯仲科,孟宪宇,韩熙春.2002b.建立我国多级分辨率的森林资源调查技术体系.北京林业大学学报,24(5/6):156-159
[16]甘淑,袁希平,何大明.2003.遥感专家分类系统在滇西北植被信息提取中的应用试验研究.云南大学学报(自然科学版),25(6):553-557
[17]高成发.1999.GPS 测量.北京:人民交通出版社
[18]郭峰,舒宁.2007.基于灰度-基元共生矩阵的多光谱遥感影像分类.测绘信息与工程,32(6):20-22
[19]贾世楼.2001.信息论理论基础.哈尔滨:哈尔滨工业大学出版社
[20]贾永红,李德仁.200t.多源遥感影像像素级融合分类与决策级分类融合法的研究.武汉大学学报信息科学版,(5):430-434
[21]瞿继双;徐德坤;王超.2004.基于结构上下文的模糊神经网络自动目标检测方法.中国图象图形学报(A 辑),19(10):1169-1174
[22]寇文正,肖燕,张瑞玺.1990.应遥感技术进行森林资源监测方法的研究.环境遥感,5(2):102-109
[23]李弼程,魏俊,彭天强.2004.遥感影像融合效果的客观分析与评价.计算机工程与科学,26(1):42-46
[24]李朝峰,王佳梁.2001.模糊控制 BP 网络的遥感图像分类方法研究.中国矿业大学学报(自然科学版),30(3):311-314
[25]李崇贵,赵宪文,斯林等.2001.用岭估计研究以 RS 和 GIS 为基础的森林郁闭度估测.林业科学,37(5).
[26]李崇贵,李春干.2004a.森林资源监测 Spot5遥感图像几何精校正方法研究.北京林业大学学报,(2):22-25
[27]李崇贵,赵宪文.2004b.林区地形起伏对 SPOT5遥感图像几何精校正的影响.北京林业大学学报,26(2):6-10
[28]李崇贵,李春干.2005a.森林资源调查林区 GPS 控制网的试验研究.林业科学,41(1):19-24
[29]李崇贵,赵宪文,李春干.2005b.森林蓄积量遥感估测理论与实现.北京:科学出版社
[30]李崇贵,陈华安,李春干.2005c.星源通掌上森林调查仪面积测量精度研究.林业科学,41(3):174-178
[31]李春干.2002.小班定位误差评价方法探讨.林业调查规划,27(4):16-17
[32]李春干,谭必增.2004.基于 GIS 的森林资源遥感调查方法研究.林业科学,40(4):40-45
[33]李春干,代华兵,李崇贵.2006.基于高分辨率卫星图像的小班勾绘精度检验.福建林学院学报,26(2):127-130
[34]李后强,王宜主,刘政凯.1997.一种适用于多类别遥感图像分类的新方法-复合神经网络分类方法.遥感学报,1(4):257-261
[35]李留瑜.1999.林业调查技术的回顾与思考.林业资源管理,(6):50-57
[36]李世玲,张富堂.2006.一种新的回归变量选择方法.计算机应用与软件,23(5):113-115
[37]李爽,丁圣彦,钱乐祥.2002.决策树分类法及其在土地覆盖分类中的应用.遥感技术与应用,17(1):6-11
[38]李小文.2005.定量遥感的发展与创新.河南大学学报(自然科学版),35(4):49-56
[39]李志林,朱庆.2003.数字高程模型.武汉:武汉大学出版社
[40]林辉.2005.基于多特征的高分辨率遥感图像分类技术研究.长沙:中南林学院博士学位论文
[41]刘亮,姜小光,李显彬,等.2006.利用高光谱遥感数据进行农作物分类方法研究.中国科学院研究生院学报,23(4):484-488
[42]刘基余,李征航,王跃虎,等.2003.全球卫星定位及其应用.北京:科学出版
[43]刘顺喜,张定祥,尤淑撑,等.2005.分区控制和分层提取下的 OMIS-I 影像土地利用/覆盖分类方法研究.地理与地理信息科学,21(5):28-32
[44]刘晓龙.2002.基于影像匹配接边纠正的数字正射影像的镶嵌技术.遥感学报,6(2):104-109
[45]刘旭升,张晓丽.2005.基于 BP 神经网络的森林植被遥感分类研究.林业资源管理,(1):51-54
[46]刘亚岚,阎守邕,王涛,等.2002.遥感图像分区自动分类方法研究.遥感学报,6(5):359-326.
[47]骆剑承,周成虎,杨艳.2000a.基于径向基函数(RBF),映射理论的遥感影像分类模型研究.中国图象图形学报,5(A 版)(2):94-99
[48]骆剑承,王钦敏,周成虎,等.2000b.基于自适应共振模型的遥感影像分类方法研究.测绘学报,31(2):145-150
[49]骆剑承,周成虎,梁怡,等.2002.支持向量机及其遥感影像空间特征提取和分类的应用研究.遥感学报,6(1):50-55
[50]明冬萍,骆剑承,周成虎,等.2005.高分辨率遥感影像信息提取及块状基元特征提取.数据采集与处理,20(1):34-39
[51]牛明香,赵庚星,李尊英.2004.南四湖湿地遥感信息分区分层提取研究.地理与地理信息科学.20(2):45-52.
[52]欧阳鑫玉,赵楠楠.2002.图像分割技术的发展.鞍山钢铁学院学报,25(5):363-368
[53]乔伟峰,孙在宏,后斌.2003.利用 SPOT5数据制作正射影像图并辅助更新土地利用现状图的方法.南京师范大学学报(自然科学版),26(3):112-116
[54]任青山,杨小林,范文义.2001.3S 技术在拉萨市森林植被调查中的应用.东北林业大学学报,29(4):18-21
[55]沙占江,马海州,李玲琴,等.2004.多尺度空间分层聚类算法在土地利用与土地覆被研究中的应用.地理科学,24(8):477-483
[56]师庆东,吕光辉,潘晓玲,等.2003.遥感影像中分区分类法及在新疆北部植被分类中的应用.干旱区地理,26(3):264-268.
[57]施益强,陈崇成,陈玲.2004.遥感技术在环境资源中的应用进展与展望,国土资源遥感,(4):7-13
[58]孙丹峰,汲长远,林培.1999.自组织网络在遥感土地覆盖分类中应用研究.遥感学报,3(2):139-143
[59]唐守正,陈永富.2006.关于森林资源监测体系改革的一些思考.见:森林可持续经营探索与实践.北京:中国林业出版社,27-32
[60]唐新明,林宗坚,吴岚.1999.基于等高线和高程点建立 DEM 的精度评价方法探讨.遥感信息,(3):7-10
[61]王海晖,彭嘉雄,吴巍,等.2003.多源遥感图像融合效果评价方法研究.计算机工程与应用,25(2):33-37
[62]王惠文.1999.偏最小二乘回归方法及其应用.北京:国防工业出版社
[63]王家耀,崔铁军,苗国强.2004.数字高程模型及其数据结构.海洋测绘,24(3):1-4
[64]王建武,肖红生,张利民.2000.GPS 在沿海围垦动态监测中的应用.华南农业大学学报,21(2):1-4.
[65]王任华,霍宏涛,游先祥.2003.人工神经网络在遥感图像森林植被分类中的应用.北京林业大学学报,25(4):1-5
[66]王松桂,陈敏,陈立萍.1999.线性统计模型线性回归与方差分析.北京:高等教育出版社
[67]王圆圆,李京.2004.遥感影像土地利用/覆盖分类方法研究综述.遥感信息,(1):53-59
[68]卫建军,李新平,赵东波,等.2006.混合像元分离的研究进展.水土保持研究,13(5):103-105
[69]翁永玲,田庆久.2003.遥感数据融合方法分析与评价综述.遥感信息,3:49-54
[70]熊轶群,吴健平.2006.面向对象的城市绿地信息提取方法研究.华东师范大学学报(自然科学版),(4):84-90
[71]徐冠华.1994.三北防护林地区再生资源遥感的理论及其技术应用.北京:中国林业出版社.
[72]徐天蜀,张王菲,岳彩荣.2007.基于 PCA 的森林生物量遥感信息模型研究.生态环境,16(6):1759-1762
[73]杨长保,丁继红.2006.面向对象的遥感图像分类方法研究.吉林大学学报(地球科学版),36(4):642-646
[74]杨丽英,覃征,张选平.2005.分类器模拟算法及其应用.西安交通大学学报,39(12):1311-1314.
[75]游先祥,李福林,王化昌,等.1995.森林资源调查、动态监测、信息管理.北京:中国林业出版社.
[76]游先祥主编.2003.遥感原理及其在资源环境中的应用.北京:中国林业出版社
[77]曾志远.2004.卫星遥感图像计算机分类与地学应用研究.北京:科学出版社
[78]张锦水,何春阳,潘耀忠,等.2006.基于 SVM 的多源信息复合的高空间分辨率遥感数据分类研究.遥感学报,10(1):49-57
[79]张路,廖明生.2006.一种顾及上下文的遥感影像模糊聚类.遥感学报,10(1):58-65
[80]张秀英,冯学智,刘伟.2007.基于多分类器结合的 IKONOS 影像城市植被类型识别.东南大学学报(自然科学版),37(3):399-403
[81]张翊涛,陈洋,王润生.2005.结合自动分区与分层分析的多光谱遥感图像地物分类方法.遥感技术与应用,20(3):332-337
[82]赵宪文,李崇贵,斯林,等.2001a.森林资源遥感估测的重要进展.中国工程科学,3(8):15-28
[83]赵宪文,李崇贵.2001b.基于“3S”的森林资源定量估测-原理、方法、应用及软件实现.北京:中国科学技术出版社
[84]赵宪文,李崇贵,斯林,等.2002.基于信息技术的森林资源调查新体系.北京林业大学学报,24(5/6):147-155
[85]赵英时等.2003.遥感应用分析原理与方法.北京:科学出版社.
[86]周前祥,敬忠良,姜世忠 2003.不同光谱与空间分辨率遥感图像融合方法的理论研究.遥感技术与应用,18(1):41-46
[87]周忠谟,易杰军,周琪.1992.GPS 卫星测量原理与应用.北京:测绘出版社
[88]朱述龙,张占睦.2000.遥感图像获取与分析.北京:科学出版社
[89]朱维凡.1999.TM 卫星图象在二类调查应用中的探讨.林业资源管理,(3):54-56
[90]ADAMS,J.B.,SMITH,M.O.and GILLESPIE,A.R.,1993,Imagine spectroscopy:interpretation based on spectral mixture analysis.In C.M.Pieters and P.A-J.Englert(Eds),Remote Geochemical Analysis:Elemental and mineralogical composition,pp.145-166(New York:Cambridge University Press).
[91]ADAMS,J.B.,SABOL,D.E.;KAPOS,V.,FILHO,R.A.,et al.,1995,Classification of multispectral images based on fractions of endmembers:application to land cover change in the Brazilian Amazon.Remote Sensing of Environment,52.pp.137-154.
[92]ARGIALAS,D.,TZOTSOS,A.2006,Automatic extraction of physiographic features and alluvial fans in Nevada,USA fiom digial elevation models and sattlite imagery through multiresolution segmentation and object-oriented classification.ASPRS 2006 Annual Conference,Prospecting for Geospatial Information Integration.1-10
[93]APLIN,P.,ATKINSON,P.M.and CURRAN,P.J.,1999a,Per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors:problems and potential solutions.In P.M.Atkinson and N.J.Tate(Eds),Advances in Remote Sensing and GISAnalysis,pp.219-239(New York:John Wiley and Sons).
[94]APLIN,P.,ATKINSON,P.M.and CURRAN,P.J.,1999b,Fine spatial resolution sedated satellite sensor imagery for land cover mapping in the United Kingdom.Remote Sensing of Environment,68.pp.206-216.
[95]APLIN,P.and ATKINSON,P.M.,2001,Sub-pixel land cover mapping for per-field classification.International Journal of Remote Sensing,22,pp.2853-2858.
[96]ASNER,G.P,and HEIDEBRECHT,K.B.,2002.Spectral unmixing of vegetation,soil and dry carbon cover in arid regions:comparing multispectral and hyperspectral observations.International Journal of Remote Sensing,23,pp.3939-3958.
[97]ATKINSON,P.M.and CURRAN,P.J.,1997.Choosing an appropriate spatial resolution for remote sensing investigations.Photogrammetric Engineering and Remote Sensing,63,PP.1345-1351.
[98]BAATZ,M.,SCH(A|¨)PE,A.,2000,Multiresolution Segmentation-an optimization approach for high quality multi-scale image segmentation;Proceedings:Angewandte Geographische Informations verarbeitung(AGIT)Ⅻ,Salzburg,Austria,Wichmann Verlag,pp12-23
[99]BATESON,A.and CURTISS,B.,1996,A method for manual endmember selection and spectral unrnixing.Remote Sensing of Environment,55,pp.229-243.
[100]BENEDIKTSSON,J.A.and KANELLOPOVLOS,I.,1999,Classification of multisource and hyperspectral data based on decision fusion.IEEE Transactions on Geoscience andRemote Sensing,37,pp.1367-1377.
[101]BENZ,U.C.,HOFMANN,P.,WILLHAUCK,G.,LINGENFELDER,I.,et al.,2004,Multi-resolution object-oriented fuzzy analysis of remote sensing data for GIS-ready information.ISPRS Journal of Photogrammetry & Remote Sensing.58,pp.239-258.
[102]BINAGHI,E.,MADELLA,P.,MONTESANO,M.G.and RAMPINI,A.,1997,Fuzzy contextual classification of multisource remote sensing images.IEEE Transactions on Geoscience and Remote Sensing,35,pp.326-339.
[103]BINAGHI,E.,BRIVIO,P.A.,GHEZZI,P.and RAMPINI,A.,1999,A fuzzy set accuracy assessment of soft classification.Pattern Recognition Letters,20,pp.935-948.
[104]BLOCH,I.,1996,Information combination operators for data fusion:a comparative review with classification.IEEE Transactions on Systems,Man,and Cybernetics,26,pp.52-67.
[105]CIHLAR,J.,2000,Land cover mapping of large areas fiom satellites:status and research priorities.International Journal of Remote Sensing,21,pp.1093-1114.
[106]COPPIN,P.;JONCKHEERE,I..,NACKAERTS.K.,MUYS,B.,et al.,2004,Digital change detection methods in ecosystem monitoring:a review.International Journal of Remote Sensing,25(9):1565-1596
[107]CORTIJO,F.J.and DE LA BUNCA,N.P.,1998,Improving classical contextual classification.International Journal of Remote Sensing,19,pp.1591-1613.
[108]CRACKNELL,A.P.,1998,Synergy in remote sensing—what's in a pixel? International Journal of Remote Sensing,19,pp.2025-2047.
[109]CUSHNIE,J.L.,1987,The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies.International Journal of Remote Sensing,8,pp.15-29.
[110]DEAN,A.M.and SMITH,G,M.,2003,An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities.International Journal of Remote Sensing,24,pp.2905-2920.
[111]DEFINIENS AG,2006a,Definiens professional 5 reference book.Ver.5.0.6.1.
[112]DEFINIENS AG,2006b,Definiens professional 7 User Guide.Ver.7.0.2.936
[113]DEFRIES,R.S.and CHAN,J.C.,2000,Multiple criteria for evaluating machine learning algorithms for land cover classification flora satellite data.Remote Sensing of Environment,74,pp.503-515.
[114]EKSTRAND,S.1996,Landsat TM-Based Forest Damage Assessment,Correction for Topographic Effects.Photogrammetric Engineering and Remote Sensing,62(2),pp.151-161.
[115]FAUVEL,M.,CHANUSSOT,J.,BENEDIKTSSON,J.A.,2006,Decision fusion for the classification of urban remote sensing images.IEEE Transactions on Geoscience and Remote Sensing,44(10),pp.2828-2838.
[116]FISHER,P.,1997,The pixel:a snare and a delusion.International Journal of Geographical Information System,7,pp.349-366.
[117]FLYGARE,A-M.,1997,A comparison of contextual classification methods using Landsat TM.International Journal of Remote Sensing,18,pp.3835-3842.
[118]FOODY,G.M.and COX,D.P.,1994,Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions.International Journal of Remote Sensing,15,pp.619-631.
[119]FOODY,G.M.,1996,Approaches for the production and evaluation of fuzzy land cover classification from remotely-sensed data.International Journal of Remote Sensing,17,pp.1317-1340.
[120]FOODY,G.M.and ARORA,M.K.,1997,An evaluation of some factors affecting the accuracy of classification by an artificial neural network.International Journal of Remote Sensing,18,pp.799-810.
[121]FOODY,G.M.,1999,Image classification with a neural network:from completely-crisp to fully-fuzzy situation.In P.M.Atkinson and N.J.Tate(Eds),Advances in Remote Sensing and GIS Analysis,pp.17-37(New York:John Wiley and Sons).
[122]FOODY,G.M.,2002,Status of land cover classification accuracy assessment.Remote Sensing of Environment,80,pp.185-201.
[123]FOODY,G.M.and MATHUR,A..2004,Arelative evaluation of multiclass image classification by support vector machines.IEEE Transactions on Geoscience and Remote Sensing,42,pp.1335-1343.
[124]FORD.G.E..and ZANELLI,C.I.,1985,Analysis and Quantification of Errors in the Geometric Correction of Satellite Images.Photogrammetric Engineering and Remote Sensing,51(11),pp.1725-1734.
[125]FRANKLIN,J.,PHINN,S.R.,WOODCOCK,C.E.and ROGAN,J.,2003,Rationale and conceptual framework for classification approaches to assess forest resources and properties.In M.A.Wulder and S.E.Franklin(Eds),Remote Sensing of Forest Environments Concepts and case studies,pp.279-300(Boston:Kluwer Academic Publishers).
[126]FRANKLIN,S.E.and WULDER.M.A.,2002,Remote sensing methods ill medium spatial resolution satellite data land cover classification of large areas.Progress In Physical Geography,26,pp.173-205.
[127]FRANKLIN,S.E.,PEDDLE,D.R..DECHKA,J.A.and STENHOUSE,G.B.,2002,Evidential reasoning with Landsat TM,DEM and GIS data for land cover classification in support of grizzly bear habitat mapping.International Journal of Remote Sensing,23,pp.4633-4652.
[128]FRIEDL,M.A.,BRODLEY,C.E.and STRAHLER,A.H.,1999,Maximizing land cover classification accuracies produced by decision trees at continental to global scales.IEEE Transactions on Geoscience and Remote Sensing,37,pp.969-977.
[129]FU,K.S.and MUI,J.K.1981,A survey of image segmentation.Pattern Recognition,13(1),pp.3-16
[130]GALLEGO,F.J.,2004,Remote sensing and land cover area estimation.International Journal of Remote Sensing,25,pp.3019-3047.
[131]GHOSH,J.,TUMER,K.,BECK,S.,et al.1995,Integration of Neural Classifiers for Passive Sonar Signals.Leondes DSP Theory and App lications.Academic Press.
[132]GITAS,I.Z.,MITRI,G.H.and VENTURA,G.,2004,Object-based image classification for burned area mapping of Creus Cape Spain,using NOAA-AVHRR imagery.Remote Sensing of Environment,92,pp.409-413.
[133]GONG,P.and HOWARTH,P.J.,1992,Frequency-based contextual classification and gray-level vector reduction for land-use identification.Photogrammetric Engineering and Remote Sensing,58,pp.423-437.
[134]GONG,P.,MARCEAU,D.J.and HOWARTH,P.J.,1992,A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data.Remote Sensing of Environment,40,pp.137-151.
[135]HARALICK,R.M.and SHAPIRO,L.G.,1985.Survey:Image Segmentation Techniques.Computer Vision,Graphics,and Image Processing,29(1):100-132.
[136]HARRIS,P.M.and VENTURA,S.J.,1995,The integration of geographic data with remotely sensed imagery to improve classification in an urban area.Photogrammetric Engineering and Remote Sensing,61,pp.993-998.
[137]HEROLD,M.,LIU,X.,and CLARKE,K.C.,2003,Spatial metrics and image texture for mapping urban land use.Photogrammetric Engineering and Remote Sensing,21,pp.2163-2183.
[138]HILL,J.,and STURM,B.,1991,Radiometric correction of multitemporal thematic mapper data for use in agricultural land-cover classification and vegetation monitoring.International Journal o f Remote Sensing,12(7):1471-1491
[139]HODGSON,M.E.,JENSEN,J.R.,TULLIS,J.A.,RJORDAN,K.D.,et al.,2003,Synergistic use lidar and color aerial photography for mapping urban parcel imperviousness.Photogrammetric Engineering and Remote Sensing,69,pp.973-980.
[140]HUANG,Z.and LEES,B.G.,2004.Combining non-parametric models for multisource predictive forest mapping.Photogrammetric Engineering and Remote Sensing,70,pp.415-425.
[141]HUANG,C,DAVIS,L.S.,and TOWNSHEND,J.R.G.,2002,An assessment of support vector machines for land cover classification.International Journal of Remote Sensing,23,pp.725-749.
[142]HUBERT-MOY,L.,COTONNEC,A.,LE DU,L.,CHARDIN,A.,et al.,2001,A comparison of parametric classification procedures of remotely sensed data applied on different landscape units.Remote Sensing of Environment,75.pp.174-187.
[143]HUGUENIN,R.L.,KARASKA,M.A.,BLARICOM,D.V.and JENSEN,J.R.,1997,Subpixel classification of Bald Cypress and Tupelo Gum trees in Thematic Mapper imagery.Photogrammetric Engineering and Remote Sensing,63,PP.717-725.
[144]IRONS,J.R.,MARKHAM.B.L.,NELSON,R.F.,TOLL,D.L.,et al.,1985,The effects of spatial resolution on the classification of Thematic Mapper data.International Journal of Remote Sensing,6,pp.1385-1403.
[145]JANSSEN,L.F.and MOLENAAR,M..1995,Terrain objects:their dynamics and their monitoring by integration of GIS and remote sensing.IEEE Transactions of Geoscience and Remote Sensing,33,pp.749-758.
[146]JANZ,K.,1993,World forest resources assessment 1990:An overview.Unasylva,44(174),pp.1-15
[147]KARTIKEYAN,B.,GOPALAKRISHNA,B.,KALUBARME,M.H.and MAIUMDER,K.L.,1994,Contextual techniques for classification of high and low resolution remote sensing data.International Journal of Remote Sensing,15.pp.1037-1051.
[148]KEUCHEL,J.,NAVMANN,S.,HEILER,M.and SIEGMUND,A.,2003,Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data.Remote Sensing of Environment,86,pp.530-541.
[149]KIM,H.,PANG,S.,JE,H.,KIM,D.,and BANG,S.Y.,2003,Constructing support vector machine ensemble.Pattern Recognition,36,pp.2757-2767.
[150]KOHL M.,2004,Forest Inventory and Monitoring.Encyclopedia afforest science,pp.403-409.
[151]KONTOES,C,WILKINSON,G.G.,BURRILL,A.,GOFFREDO,S.,et al.,1993,An experimental system for the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture.International Journal of Geographical Information Systems,7,pp.247-262.
[152]KOROTKOV,A.V.,PECK,T.J.,1993,Forest resources of the industrialized countries:An ECE/FAO assessment.Unasylva,44(174),pp.1-31.
[153]KULKARNI,A.D.and LULLA,K.,1999,Fuzzy neural network models for supervised classification: multispectral image analysis.Geocarto International.14,pp.42-50.;154]LAWRENCE,R.,BUNN,A.,POWELL,S.and ZMABON,M.,2004,Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis.Remote Sensing of Environment,90,pp.331-336.
[155]LIU,W.,GOPAL,S.and WOODCOOCK,C.E.,2004,Uncertainty and confidence in land cover classification using a hybrid classifier approach.Photogrammetric Engineering and Remote Sensing,70,pp.963-972.
[156]LLOYD,CD.,BERBEROGLU,S.,CURRAN,P.J.and ATKINSON,P.M.,2004,A comparison of texture measures for the per-field classification of Mediterranean land cover.International Journal of Remote Sensing,25,pp.3943-3965.
[157]LOBO,A.,CHIC,O.and CASTERAD,A.,1996,Classification of Mediterranean crops with multisensor data:per-pixel versus per-object statistics and image segmentation.International Journal of Remote Sensing,17,pp.2385-2400.
[158]LU,D.,MORAN,E.and BATISTELM,M.,2003,Linear mixture model applied to Amazonian vegetation classification.Remote Sensing of Environment,87,pp.456-469.
[159]LU,D.and WENG,Q.,2004,Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+ imagery.Photogrammetric Engineering and Remote Sensing,70,pp.1053-1062.
[160]LU D.,WENG Q.,2007,A survey of image classification methods and techniques of improving classification performance.International Journal of Remote Sensing,38(5-6),pp.823-870
[161]MAGUSSEN,S.,BOUDEWYN,P.and WULDER,M.,2004,Contextual classification of Landsat TM images to forest inventory cover types.International Journal of Remote Sensing,25,pp.2421-2440.
[162]MANNAN,B.and RAY,A.K.,2003,Crisp and fuzzy competitive learning networks for supervised classification of multispectral IRS scenes.International Journal of Remote Sensing,24,pp.3491-3502.
[163]MANNAN,B.,ROY,J.and RAY,A.K.,1998,Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images.International Journal of Remote Sensing,19,pp.767-774.
[164]MASELLI,F.,2001,Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analysis.Remote Sensing of Environment,75,pp.29-38.
[165]MICHAEL,A.W.,WERNER A.K.,MARK,G.,2004,National level forest monitoring and modeling in Canada.Progress in Planning,61 (May),pp.365-381
[166]MICHAEL,S.C,FRANKLIN,S.E.and MICHAEL,A.W.,2006,Object-based Analysis of Ikonos-2 Imagery for Extraction of Forest Inventory Parameters.Photogrammetric Engineering & Remote Sensing,72(4),pp.383-394.
[167]MICHELSON,D.B.,LJLJEBERG,B.M.and PILESJO,P.,2000,Comparison of algorithms for classifying Swedish land cover using Landsat TM and ERS-1 SAR data.Remote Sensing of Environment,71,pp.1-15.
[168]MYINT,S.W.,2001,A robust texture analysis and classification approach for urban land-use and land-cover feature discrimination.Geocarto International,16,pp.27-38.
[169]NEVILLE,R.A.,LEVESQUE,J.,SUENE,K.,NADEAV,C,et ah,2003,Spectral unmixing of hyperspectral imagery for mineral exploration:comparison of results from SFSI and AVIRIS.Canadian Journal of Remote Sensing,29,pp.99-110.
[170]OKTN,G.S.,ROBERTS,D.A.,MURRAY,B.and OKIN,W.J.,2001,Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments.Remote Sensing of Environment,77,pp.212-225.
[171]OLTHOF,I.,KING,D.J.and LAUTENSCHLAGER,R.A.,2004,Mapping deciduous forest ice storm damage using Landsat and environmental data.Remote Sensing of Environment,89,pp.48-96.
[172]PAL,M.and MATHER,P.M.,2003,An assessment of the effectiveness of decision tree methods for land cover classification.Remote Sensing of Environment,86,pp.554-565.
[173]PAL,M.and MATHER,P.M.,2004,Assessment of the effectiveness of support vector machines for hyperspectral data.Future Generation Computer System,20,pp.1215-1225.
[174]PAL,N.R.and PAL,S.K.1993,A review on image segmentation techniques.Pattern recognition,26(9),pp.1277-1294
[175]PAOLA,J.D.and SCHOWENGERDT,R.A.,1995,A review and analysis of back propagation neural networks for classification of remotely sensed multispectral imagery.International Journal of Remote Sensing,16,pp.3033-3058.
[176]PEDDLE,D.R.and FERGUSON,D.T.,2002,Optimization of multisource data analysis:an example using evidential reasoning for GIS data classification.Computer & Geosciences,28,pp.45-52.
[177]PEDLEY,M.I.and CURRAN,P.I..1991,Per-field classification of an example using SPOT HRV imagery.International Journal of Remote Sensing,12,pp.2181-2192.
[178]PENALOZA,M.A.and WELCH,R.M.,1996,Feature selection for classification of polar regions using a fuzzy expert system.Remote Sensing of Environment,58,pp.81-100.
[179]PLATT,R.V.and GOETZ,A.F.H.,2004,A comparison of AVIRIS and Landsat for land use classification at the urban fringe.P hotogrammetric Engineering and Remote Sensing,70,pp.813-819.
[180]PRICE,K.P.,GUO,X.and STILES,J.M.,2002,Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas.International Journal of Remote Sensing,23,pp.5031-5042.
[181]REESE,H.,NILSSON,M.,PAHLEN,TG.,TINGELOF,U.,2003,Countrywide estimates of forest variables using satellite data and field data from the national forest inventory.Ambio,32(8),pp.542-548.
[182]RGIALAS,D.,TZOTSOS,A.,2006,Automatic extraction of physiographic feature and alluvial fans in Nevada,USA from digial elevation models and satellite imagery through multiresolution segmentation and object-oriented classification.ASPRS 2006 Annual Conference Reno,Nevada May 1-5.
[183]ROBERTS,D.A.,BATJSTA,G.T.,PEREIRA,J.L.G.,WALLER,E.K.,et al.,1998a,Change identification using multiemporal spectral mixture analysis:applications in eastern Amazonia.In R.S.Lunetta and C.D.Elvidge(Eds),Remote Sensing Change Detection:Environment Monitoring Methods and Applications,pp.137-161(Chelsea,MI:Ann Arbor Press).
[184]ROBERTS,D.A.,GARDNER,M.,CHURCH,R.,USTIN,S.,et al.,Ⅰ998b,Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models.Remote Sensing of Environment,65,pp.267-279.
[185]ROLI,F.,GIACINTO,G.,VEMAZZA,G.,1997,Comparison and combination of statistical and neural network alogrithms for remote sensing image classification.Kanellopoulos,Wilkinson G.,Roli,F.,et al.,Neurocomputation in Remote Sensing Data Anaylysis.Springer.
[186]SCHIEVE,J.,TUFTE,L.and Ehlers,M.,2001,Potential and problems of multi-scale segmentation methods in remote sensing.GeoBIT/GIS 6,pp.34-39.
[187]SCHOWENGERDT,R.A.,1996,On the estimation of spatial-spectral mixing with classifier likelihood functions.Pattern Recognition Letters,17,pp.1379-1387.
[188]SHALAN,M.A.,ARORA,M.K.and GHOSH,S.K.,2003,An evaluation of fuzzy classifications from IRS1C LISS Ⅲ imagery:a case study.International Journal of Remote Sensing,24,pp.3179-3186.
[189]SHARMA,K.M.S.and SARKAR,A.,1998,A modified contextual classification technique for remote sensing data.Photogrammetric Engineering and Remote Sensing,64,pp.273-280.
[190]Siev(a|¨)nen,T.2004.Recreation:Inventory,monitoring and management.In:Burley,J.,Evans,J.& Youngquist,J.A.(eds.).Encyclopedia offorest sciences.Elsevier,pp.958-964.
[191]SMALL,C.,2004,The Landsat ETM+spectral mixing space.Remote Sensing of Environment,93,pp.1-17.
[192]SMITH,W.B.,2002,Forest inventory and analysis:a national inventory and monitoring program.Environmental Pollution.116(suppl 1):S233-S242.
[193]SMITH,M.O.,USTIN,S.L.,ADAMS,J.B.and GILLESPIE,A.R.,1990,Vegetation in Deserts:Ⅰ.A regional measure of abundance flom multispectral images.Remote Sensing of Environment,31,pp.1-26.
[194]SOUTH,S.,QI,J.and LUSCH,D.P.,2004,Optimal classification methods for mapping agricultural tillage practices.Remote Sensing of Environment,91,pp.90-97.
[195]STEELE,B.M.,2000,Combining multiple classifiers:an application using spatial and remotely sensed information for land cover type mapping.Remote Sensing of Environment,74,pp.545-556.
[196]STUCKENS,J.,COPPIN,P.R.and BAUER,M.E.,2000,Integrating contextual information with per-pixel classification for improved land cover classification.Remote Sensing of Environment,71,pp.282-296.
[197]SUEN,C.Y.,LAM,L.,2000,Multiple classifier combination methodologies for differnent output levels.Kittler,Roli.Proceedings of First International Workshop on Multiple Classifier Systems,Springer.1857,pp.52-66.
[198]SUGUAMRAN.R.,ZERR,D.and PRATO,T.,2002,Improved urban land cover mapping using multitemporal IKONOS images for local government planning.Canadian Journal of Remote Sensing,28,pp.90-95.
[199]THOMAS,N.,HENDRIX.C.and CONGALTON,R.G.,2003,A comparison of urban mapping methods using high-resolution digital imagery.Photogrammetric Engineering and Remote Sensing,69,pp.963-972.
[200]VAGEN,T.-G.,2006,Remote sensing of complex land use change trajectories-a case study from the highlands of Madagascar.Agriculture,Ecosystems and Environment,115,pp.219-228.
[201]WANG,L.,SOUSA,W.P.,GONG,P.and BIGING,G.S.,2004,Comparison of IKONOS and Quick.Bird images for mapping mangrove species on the Caribbean coast of panama.Remote Sensing of Environment,91,pp.432-440.
[202]WOODCOCK,C.E.and GOPAL,S.,2000,Fuzzy set theory and thematic maps:accuracy assessment and area estimation.International Journal of Geographical Information Science,14,pp.153-172.
[203]XU,L.,KRZYZAK,A.,SUEN C,Y.,1992,Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition.IEEE Transaction on Systems,Man,and Cybernetics,22(3),pp.418-435.
[204]XU,B.,GONG,P.,SETO.E.and SPEAR,R.,2003,Comparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic IKONOS image.Photogrammetric Engineering and Remote Sensing,69,pp.529-536.
[205]YU,Q.,GONG,P.,CLINTON,N.,BIGIBG,G.et al.,2006,Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery.Photogrammetric Engineering and remote sensing,72(7),pp.799-811
[206]HUANG,X.,ENGEL,B.A.,XIONG,X.and JOHANNSEN,C.J.,1995,Analysis of classification results of remotely sensed data and evaluation of classification algorithms.Photogrammetric Engineering and Remote Sensing,61,pp.427-433.
[207]ZHANG,Y.,1994,Optimisation of building detection in satellite images by combining multispectral classification and texture filtering.ISPRS Journal of Photogrammetry andRemote Sensing,54,pp.50-60.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |