面向对象的林地信息提取研究
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摘要
林地与人们的生产、生活密切相关,具有净化空气、涵养水源、调节生态平衡等重要作用。因此,在城市规划的编制中,要求加强保护、科学开发林地资源,促进经济、社会、环境的和谐发展。但是随着城市化进程的加快,林地资源不断遭到破坏,实时监控林地变化至关重要。
由于遥感具有全天候、时效性等优点,遥感影像专题信息提取已成为获取信息的主要手段。目前大多仅对绿地(包含林地、草地等)进行提取,且多采用目视解译或传统基于像元的方法,没有单独提取林地信息。本文在总结了绿地信息提取研究现状的基础上,基于三种不同分辨率的遥感影像(ETM、SPOT5、QuickBird影像),采用面向对象信息提取技术实现了林地信息提取。
本文概述了遥感原理与发展历程,归纳总结了遥感影像信息提取技术和绿地信息提取研究现状。介绍了面向对象信息提取方法、研究区域概况,以及ETM、SPOT5、QuickBird影像的特征,并对影像作了正射校正、配准、融合等处理。以昆明市某区域为例,通过分析林地在三类影像中的典型特征和最优分割尺度,采用面向对象方法研究林地信息的提取,并对三种提取结果进行了精度评价。同时,对三类影像采用传统基于像元的方法(最大似然法)提取林地信息,并进行了精度评价,对比分析了传统基于像元方法(最大似然法)的提取结果与面向对象方法的提取结果,从而得出有益的结论,面向对象方法在高分辨率遥感影像信息提取中具有明显优势。
将林地信息提取结果应用到城市规划编制中,通过计算林地覆盖度,结合坡度信息,划定了“三区”,即“禁建区”、“限建区”、“适建区”,辅助了城市规划决策,有利于科学地保护、开发林地资源。本文以昆明市某区域为例,基于高分辨率遥感影像,使用面向对象方法,通过选择林地的最优分割尺度以及科学分析林地的典型特征,实现了林地信息准确、高效、经济的提取,同时实现了林地信息的快速更新。
Forest plays an important role in people's life. It can purify the air, conserve warter, and regulate the ecological banlance. However, as the urbanization process accelerating, the forest resource has been destroyed. It becomes more and more important to monitor the forest resource timely. And in the urban planning, the forest should be protected and dveloped scientifically. This can ensure the development of economy society and environment harmonically.
Because the RS is all-day all-weather and timeliness, the RS image has become the important data to obtain the thematic information. At present, only the vegetation is extracted from the RS image based on the visual interpretation or traditional pixel-based approach, including wooldland and grassland. The forest information is not extracted solely most of the time. On the basis of summarizing the status of vegetation information extraction, the paper extracts the forest information using the object-oriented method from three different resolution RS images (ie, ETM, SPOT5, QuickBird image).
Firstly, the thesis outlines the history and development of RS, and sumamarizes the information extraction technologies and vegetation information extraction status. Sencondly, it introduces the object-oriented information extraction method, the overview of the study area in Kunming city and the characteristics of the ETM SPOT5 and QuickBird image, and then does ortho generation, fusion and clipping to the three kinds of images. Thirdly, in the study area, it analyses the forest typical characteristics and the best segmentation scales in the three kinds of images, and then studies the forest information extraction used the object-oriented method. Then, it makes the accurancy analysis. Fourthly, it extracts the forest information using the traditional method (maximum likelihood method), and makes the comparison in the forest information extraction results.
In the county in Kunming city, using the object-oriented method and the high resolution RS image, the paper extracts the forest information accurately efficiently and economically based on the typical characters and the best segmentation scale. And the forest information can be updated in time. After calculating the forest coverage in QuickBird image, it demarcrates the banned construction area, restricted construction area, and allowed construction area using the forest coverage and the slope. This can assist the urban planning decisions. And it is good for protecting the forest resouse and developing the resouse scientifically.
由于遥感具有全天候、时效性等优点,遥感影像专题信息提取已成为获取信息的主要手段。目前大多仅对绿地(包含林地、草地等)进行提取,且多采用目视解译或传统基于像元的方法,没有单独提取林地信息。本文在总结了绿地信息提取研究现状的基础上,基于三种不同分辨率的遥感影像(ETM、SPOT5、QuickBird影像),采用面向对象信息提取技术实现了林地信息提取。
本文概述了遥感原理与发展历程,归纳总结了遥感影像信息提取技术和绿地信息提取研究现状。介绍了面向对象信息提取方法、研究区域概况,以及ETM、SPOT5、QuickBird影像的特征,并对影像作了正射校正、配准、融合等处理。以昆明市某区域为例,通过分析林地在三类影像中的典型特征和最优分割尺度,采用面向对象方法研究林地信息的提取,并对三种提取结果进行了精度评价。同时,对三类影像采用传统基于像元的方法(最大似然法)提取林地信息,并进行了精度评价,对比分析了传统基于像元方法(最大似然法)的提取结果与面向对象方法的提取结果,从而得出有益的结论,面向对象方法在高分辨率遥感影像信息提取中具有明显优势。
将林地信息提取结果应用到城市规划编制中,通过计算林地覆盖度,结合坡度信息,划定了“三区”,即“禁建区”、“限建区”、“适建区”,辅助了城市规划决策,有利于科学地保护、开发林地资源。本文以昆明市某区域为例,基于高分辨率遥感影像,使用面向对象方法,通过选择林地的最优分割尺度以及科学分析林地的典型特征,实现了林地信息准确、高效、经济的提取,同时实现了林地信息的快速更新。
Forest plays an important role in people's life. It can purify the air, conserve warter, and regulate the ecological banlance. However, as the urbanization process accelerating, the forest resource has been destroyed. It becomes more and more important to monitor the forest resource timely. And in the urban planning, the forest should be protected and dveloped scientifically. This can ensure the development of economy society and environment harmonically.
Because the RS is all-day all-weather and timeliness, the RS image has become the important data to obtain the thematic information. At present, only the vegetation is extracted from the RS image based on the visual interpretation or traditional pixel-based approach, including wooldland and grassland. The forest information is not extracted solely most of the time. On the basis of summarizing the status of vegetation information extraction, the paper extracts the forest information using the object-oriented method from three different resolution RS images (ie, ETM, SPOT5, QuickBird image).
Firstly, the thesis outlines the history and development of RS, and sumamarizes the information extraction technologies and vegetation information extraction status. Sencondly, it introduces the object-oriented information extraction method, the overview of the study area in Kunming city and the characteristics of the ETM SPOT5 and QuickBird image, and then does ortho generation, fusion and clipping to the three kinds of images. Thirdly, in the study area, it analyses the forest typical characteristics and the best segmentation scales in the three kinds of images, and then studies the forest information extraction used the object-oriented method. Then, it makes the accurancy analysis. Fourthly, it extracts the forest information using the traditional method (maximum likelihood method), and makes the comparison in the forest information extraction results.
In the county in Kunming city, using the object-oriented method and the high resolution RS image, the paper extracts the forest information accurately efficiently and economically based on the typical characters and the best segmentation scale. And the forest information can be updated in time. After calculating the forest coverage in QuickBird image, it demarcrates the banned construction area, restricted construction area, and allowed construction area using the forest coverage and the slope. This can assist the urban planning decisions. And it is good for protecting the forest resouse and developing the resouse scientifically.
引文
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[3]李德仁.论21世纪遥感与GIS的发展[J].武汉大学学报(信息科学版),2003,2：127-131.
[4]杨桄,刘湘南.遥感影像解译的研究现状和发展趋势[J].国土资源遥感,2004,2：7-10.
[5]Suha Berveroglu, et a.1 Mapping and monitoring of coastal wet lands of Cukurova Delta in the Eastern Mediterranean region[J]. Biodiversity and Conservation,2004, (13):615-633.
[6]张海龙,蒋建军,吴宏安,等.SAR与TM影像融合及在BP神经网络分类中的应用[J].测绘学报,2006,35(3)：229-233.
[7]尤淑撑,张玮,严泰来.模糊分类技术在作物类型识别中的应用[J].国土资源遥感,2000,1(43)：39-43.
[8]吴连喜,王茂新.一种改进的最大似然法用于地物识别[J].农业工程学报,2003,19(4)：54-57.
[9]邓巍,张录达,何雄奎,等.基于支持向量机的玉米苗期田间杂草光谱识别[J].光谱学与光谱分析,29(7)：1906-1910.
[10]金向军,张勇,谢云飞,等.基于支持向量机及小波变换的人参红外光谱分析[J].光谱学与光谱分析,2009,29(3)：656-660.
[11]吴均,赵忠明.利用基于小波的尺度共生矩阵进行纹理分析[J].遥感学报,2001,5(2)：100-103.
[12]吴高洪,章毓晋,林行刚.基于分形的自然纹理自相关描述和分类[J].清华大学学报(自然科学版),2000,40(3)：90-93.
[13]苏俊英,曹辉,张剑清.高分辨率遥感影像上居民地半自动提取研究[J].武汉大学学报(信息科学版),2004,29(9)：791-795.
[14]Ketting, R., L,, Landgrebe, D. A. Computer classification of remotely sensed multi-Spectral image data by extraction and classification of homogenous object[J]. IEEE Transactions on Geo-SCience Electronics,1976,14(1):19-26.
[15]张振勇,王萍,朱鲁,等.eCognition技术在高分辨率遥感影像信息提取中的应用[J].信息技术,2007,2：15-17.
[16]Argialis D P, Harlow C A. Computational image interpretation models:An overview and a perspective[J]. Photogrammetric Engineering and emote Sensing,1990,56(6):871-886.
[17]Ton J, Sticklen J, Jain A K. Knowledge-Based segmentation of Land-sat images [J]. IEEE Transactions on Geo-science and Remote Sensing,1991,29(2):223-231.
[18]Baatz M. Schape A. Object-Oriented and multi-scale image analysis in semantic networks[A]. In:proc of the 2nd International Symposium on Operationalization of Remote Sensing[C]. August 16-20th 1999. Enschede ITC.
[19]Willnauck, G., Ben Z, U. C., and Siegert, F. Semiautomatic classification procedures for Fire Monitoring Using Multi-temporal SAR images and NoAVHRR hotspot data[J], Proceedings of the 4th European conference Symthetic Apertture Radar, Cologne, Germany,2002,4-6.
[20]Zhang X. Y., Feng X. Z. Detecting Urban Vegetation Using object-oriented Method from IKONOS imagery [J]. IGARS,2005.
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