毛竹专题信息高光谱特征指数反演技术研究
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摘要
竹产业的迅速发展迫切要求遥感技术提供给其快速、准确的毛竹空间分布信息。高光谱遥感作为遥感科学的研究前沿和热点,具有图像——光谱合一的特点,能够克服多光谱遥感的不足,可通过其精细光谱优势提高毛竹专题信息提取的精度和准确性。当前可利用的高光谱遥感数据源少,尤其在南方地区受地形复杂、气候多变等因素影响获取的影像数据质量往往难以满足植被识别的要求,且影像成像空间范围小;而多光谱数据源丰富,影像成像空间范围大。为充分利用多光谱和高光谱的优势,开展基于多光谱的高光谱特征指数反演技术研究是具有现实意义的。同时,植物的光谱特征都是由其化学和形态学特征决定的,使得基于多光谱的高光谱特征指数反演成为可能。本文以福建省闽清、德化、永泰县的部分区域为研究区,以开窗口方式建立研究对象,利用HyperionEO-1高光谱遥感数据和TM多光谱数据,围绕毛竹专题信息高光谱特征指数反演技术这一主题,开展了高光谱数据降维,识别毛竹的最佳高光谱特征确定,基于识别毛竹最佳高光谱特征、TM影像数据、相关的植被指数、地形因子等遥感信息的高光谱特征指数模型构建,基于高光谱特征指数的毛竹专题信息提取等研究,得到如下结论:
(1)高光谱遥感数据分析可得:可用于研究的高光谱共计122个波段,其中31~50、54~68、75~89、94~122波段子集包含的信息量较多,是理想的候选波段子集,波段122是最优波段。光谱特性上,在任一波段上经济林与其它地物的可分性大;在1~25波段、31~50波段、58~66波段、74~85波段、92~122波段、80~85波段,竹林、杉木、阔叶林、经济林的DN值差异较明显是理想的波段子集,26~30波段、51~57波段、67~73波段、86-91波段重叠现象严重。
(2)采用波段指数法、自适应波段选择法、均值间的标准距离法、OIF指数法、主成分分析法等方法对高光谱数据进行降维,得到的分类特征组合分别为:36、63、122波段组合;63、111、122波段组合;6、97、122波段组合;44、82、122波段组合;可见光区间的第一主成分(Y1)、近红外区间的第二主成分(Y4)、短波红外区间的第二主成分(Y6)组合。
(3)各组合分类特征的分类结果表明:基于主成分分析法选择的分类特征分类的总精度和毛竹精度最高,总精度为80.36%,毛竹精度为88.70%;基于波段指数法、自适应波段选择法、均值标准距离法和OIF指数法选择的分类特征分类的总精度分别为64.97%、65.66%、71.29%、66.62%;毛竹精度分别为71.75%、72.32%、82.49%、72.32%。即确定主成分分析法选择的分类特征(Y1、Y4和Y6)作为识别毛竹的最佳特征,并作为构建模型的因变量。
(4)分析了毛竹与其它树种在多光谱(TM)、植被指数、地形因子等上的差异,在此基础上,选择TM(1、2、3、4、5、7等波段)、植被指数(NDVI、RVI、PVI)、高程、坡度和坡向为自变量,Y1、Y4、Y6为因变量,利用逐步回归,构建高光谱特征指数模型,分别为:其中Y1、Y4、Y6为高光谱中能有效识别毛竹的分类特征,Z6是Y6经过Box-Cox变换的结果;B1、B2、B3、B4为TM影像的第一、二、三、四波段;RVI为比值植被指数;NDVI为归一化比值植被指数;高程3为高程800~1200 m;缓坡为坡度6~15°;陡坡为坡度26~35°;急坡为坡度36~45°。
(5)通过所建立的指数模型,尝试了对高光谱特征的反演,并在反演结果图上进行了毛竹专题信息的提取,分类结果与传统基于多光谱数据的分类比较获得了较高的分类精度,具体为:基于高光谱特征指数分类的总精度为73.18%,Kappa系数为0.6552;基于TM原始数据分类的总精度为62.48%,Kappa系数为0.5170;二者比较,前者比后者总精度提高了10.70个百分点,Kappa系数提高了0.1382。同时,前者的毛竹分类精度达到86.52%,而后者的毛竹分类精度为74.47%,前者比后者毛竹精度提高了12.05个百分点,其它树种的分类精度也都有不同程度的提高,其中,杉木精度提高了43.44个百分点,马尾松精度提高了5.73个百分点,阔叶林精度提高了1.51个百分点,经济林精度提高了9.41个百分点。
The rapid development of bamboo industry demands the fast and accurate spatial distribution information of moso bamboo supported by remote sensing technology. As the research front and hotspot of RS science, the hyperspectral remote sensing has the characteristic of image-spectrum unity. It can overcome the shortage of multi-spectral RS; improve bamboo classification accuracy and veracity with its advantage of fine spectrum. Currently, there is few available hyperspectral image, especially in the southern area where the image is influenced by the complex terrain, variable climate and other factors, the obtained image quality often difficultly meet the need of vegetation identification and the coverage is too small. However, the multi-spectral data source is so abundant and its image courage is large. In order to take full advantage of multi-spectrum and hyperspectrum, it is practical significant to do the hypersepctral retrieval research based on the multi-spectrum. Meanwhile, all of the spectral features of vegetation are determined by the chemical and morphological characteristics, making the hyperspectral retrieval based on multi-spectrum possible.Focus on the theme of hyperspectral feature index retrieval of Bamboo thematic information, this paper 1) analyzed the dimensionality reduction for hyperspectral data, 2) identified the bamboo hyperspectral feature, then made an extraction, 3) modeled the hyperspectral feature index with hyperspectral feature optimal index, TM bands, various vegetation indices, and topographic factors etc., and 4) extracted the bamboo thematic information based on the hyperspectral feature optimal index from HyperionEO-1 remote sensing data and TM multi-spectral data in Fujian, included Minqing, Dehua, and Yongtai countries. The conclusions are as follow:
(1) Through the analysis of hyperspectral remote sensing images, we can see that the bands available sums to 122, in which Band 31~50, 54~68, 75~89, 94~122 contain more information. These bands are good candidates and Band 122 is the best one. In the spectral features, there is large separability between economic forest and other bodies at any band. The DN value difference among bamboo, Chinese fir, broadleaves and economic forest is obvious at Band 1~25, 31~50, 58~66, 74~8, 92~122, 80~85, showing that these bands are good candidates. Nevertheless, they overlap each other terribly at Band 26~30, 51~57, 67~73, 86~91.
(2) Five methods are used for dimension reduction including Band Index Method, Adaptive Band Selection Method, Standard Distance Between Means Method, OIF Method, Principal Component Analysis Method. The resulting classification feature combinations are Band Combination 36, 63, 122, Band Combination 63, 111, 122, Band Combination 6, 97, 122, Band Combination 44, 82, 122 and combination of the first component (Y1) at visible light area, the second component (Y2) at near-infrared area, the second component (Y6) at short-wave infrared area.
(3) The classification results based on each feature combination show that: the overall accuracy and moso bamboo’s accuracy based on PCA are the highest, with these two value of 80.36% and 88.70%; the overall accuracies based on Band Index Method, Adaptive Band Selection Method, Standard Distance Between Means Method, OIF Method are 64.97%, 65.66%, 71.29%, 66.62% respectively; the accuracies of moso bamboo are 71.75%, 72.32%, 82.49%, 72.32% respectively. Above all, the classified features of Y1, Y4 and Y6 selected with PCA are the best ones. In the paper, these three perform the dependent variables for model contruction. (4) Relying on the spectrum difference analysis on multi-spectral images, vegetation indices, terrain factors and tree spices such as moso bamboo, the paper selected TM (1,2,3,4,5,7, etc. bands), vegetation indexs (NDVI, RVI, PVI), elevation, slope and aspect as the independent variable, Y1, Y4, Y6 as the dependent variable,based on the factor screening, stepwise regression is used to construct the index model which can retrieve the hyperspectral characteristics. The models are : where Y1, Y4, Y6 are available classification features for moso bamboo identification in the hyperspectrum, Z6 is the result of Y6 with Box-Cox Transformation, B1, B2, B3, B4 are Band1 to Band4 of TM images, RVI is ratio vegetation index, NDVI is normalized difference vegetation index, Elevation3 is the elevation from 800 to 1200 m, Slight-slope is the slope from 6 to 15°, Abrupt-slope is the slope from 26 to 35°, Steep-slope is the slope from 36 to 45°.
(5) According the index model construction, the hyperspectral characteristics retrieval is experimented. It extracted moso bamboo information with the retrieval results. This classification accuracy is higher than the traditional method based on multi-spectrum data. The overall accuracy based on indices from retrieval is 73.18% with the Kappa of 0.6552, which the accuracy based on original TM data is 62.48% with the Kappa of 0.5170. Compared with them, we can see that the former one is 10.70% and 0.1382 higher than the latter one. Meanwhile, the former moso bamboo accuracy reaches 86.52% and the latter one 74.47%, 12.05% higher. The other species’accuracies also have been raised varying degrees, of which Chinese fir raised 43.44%, masson pine 5.73%, broadleaves 1.51% and economic forest 9.41%.
(1)高光谱遥感数据分析可得:可用于研究的高光谱共计122个波段,其中31~50、54~68、75~89、94~122波段子集包含的信息量较多,是理想的候选波段子集,波段122是最优波段。光谱特性上,在任一波段上经济林与其它地物的可分性大;在1~25波段、31~50波段、58~66波段、74~85波段、92~122波段、80~85波段,竹林、杉木、阔叶林、经济林的DN值差异较明显是理想的波段子集,26~30波段、51~57波段、67~73波段、86-91波段重叠现象严重。
(2)采用波段指数法、自适应波段选择法、均值间的标准距离法、OIF指数法、主成分分析法等方法对高光谱数据进行降维,得到的分类特征组合分别为:36、63、122波段组合;63、111、122波段组合;6、97、122波段组合;44、82、122波段组合;可见光区间的第一主成分(Y1)、近红外区间的第二主成分(Y4)、短波红外区间的第二主成分(Y6)组合。
(3)各组合分类特征的分类结果表明:基于主成分分析法选择的分类特征分类的总精度和毛竹精度最高,总精度为80.36%,毛竹精度为88.70%;基于波段指数法、自适应波段选择法、均值标准距离法和OIF指数法选择的分类特征分类的总精度分别为64.97%、65.66%、71.29%、66.62%;毛竹精度分别为71.75%、72.32%、82.49%、72.32%。即确定主成分分析法选择的分类特征(Y1、Y4和Y6)作为识别毛竹的最佳特征,并作为构建模型的因变量。
(4)分析了毛竹与其它树种在多光谱(TM)、植被指数、地形因子等上的差异,在此基础上,选择TM(1、2、3、4、5、7等波段)、植被指数(NDVI、RVI、PVI)、高程、坡度和坡向为自变量,Y1、Y4、Y6为因变量,利用逐步回归,构建高光谱特征指数模型,分别为:其中Y1、Y4、Y6为高光谱中能有效识别毛竹的分类特征,Z6是Y6经过Box-Cox变换的结果;B1、B2、B3、B4为TM影像的第一、二、三、四波段;RVI为比值植被指数;NDVI为归一化比值植被指数;高程3为高程800~1200 m;缓坡为坡度6~15°;陡坡为坡度26~35°;急坡为坡度36~45°。
(5)通过所建立的指数模型,尝试了对高光谱特征的反演,并在反演结果图上进行了毛竹专题信息的提取,分类结果与传统基于多光谱数据的分类比较获得了较高的分类精度,具体为:基于高光谱特征指数分类的总精度为73.18%,Kappa系数为0.6552;基于TM原始数据分类的总精度为62.48%,Kappa系数为0.5170;二者比较,前者比后者总精度提高了10.70个百分点,Kappa系数提高了0.1382。同时,前者的毛竹分类精度达到86.52%,而后者的毛竹分类精度为74.47%,前者比后者毛竹精度提高了12.05个百分点,其它树种的分类精度也都有不同程度的提高,其中,杉木精度提高了43.44个百分点,马尾松精度提高了5.73个百分点,阔叶林精度提高了1.51个百分点,经济林精度提高了9.41个百分点。
The rapid development of bamboo industry demands the fast and accurate spatial distribution information of moso bamboo supported by remote sensing technology. As the research front and hotspot of RS science, the hyperspectral remote sensing has the characteristic of image-spectrum unity. It can overcome the shortage of multi-spectral RS; improve bamboo classification accuracy and veracity with its advantage of fine spectrum. Currently, there is few available hyperspectral image, especially in the southern area where the image is influenced by the complex terrain, variable climate and other factors, the obtained image quality often difficultly meet the need of vegetation identification and the coverage is too small. However, the multi-spectral data source is so abundant and its image courage is large. In order to take full advantage of multi-spectrum and hyperspectrum, it is practical significant to do the hypersepctral retrieval research based on the multi-spectrum. Meanwhile, all of the spectral features of vegetation are determined by the chemical and morphological characteristics, making the hyperspectral retrieval based on multi-spectrum possible.Focus on the theme of hyperspectral feature index retrieval of Bamboo thematic information, this paper 1) analyzed the dimensionality reduction for hyperspectral data, 2) identified the bamboo hyperspectral feature, then made an extraction, 3) modeled the hyperspectral feature index with hyperspectral feature optimal index, TM bands, various vegetation indices, and topographic factors etc., and 4) extracted the bamboo thematic information based on the hyperspectral feature optimal index from HyperionEO-1 remote sensing data and TM multi-spectral data in Fujian, included Minqing, Dehua, and Yongtai countries. The conclusions are as follow:
(1) Through the analysis of hyperspectral remote sensing images, we can see that the bands available sums to 122, in which Band 31~50, 54~68, 75~89, 94~122 contain more information. These bands are good candidates and Band 122 is the best one. In the spectral features, there is large separability between economic forest and other bodies at any band. The DN value difference among bamboo, Chinese fir, broadleaves and economic forest is obvious at Band 1~25, 31~50, 58~66, 74~8, 92~122, 80~85, showing that these bands are good candidates. Nevertheless, they overlap each other terribly at Band 26~30, 51~57, 67~73, 86~91.
(2) Five methods are used for dimension reduction including Band Index Method, Adaptive Band Selection Method, Standard Distance Between Means Method, OIF Method, Principal Component Analysis Method. The resulting classification feature combinations are Band Combination 36, 63, 122, Band Combination 63, 111, 122, Band Combination 6, 97, 122, Band Combination 44, 82, 122 and combination of the first component (Y1) at visible light area, the second component (Y2) at near-infrared area, the second component (Y6) at short-wave infrared area.
(3) The classification results based on each feature combination show that: the overall accuracy and moso bamboo’s accuracy based on PCA are the highest, with these two value of 80.36% and 88.70%; the overall accuracies based on Band Index Method, Adaptive Band Selection Method, Standard Distance Between Means Method, OIF Method are 64.97%, 65.66%, 71.29%, 66.62% respectively; the accuracies of moso bamboo are 71.75%, 72.32%, 82.49%, 72.32% respectively. Above all, the classified features of Y1, Y4 and Y6 selected with PCA are the best ones. In the paper, these three perform the dependent variables for model contruction. (4) Relying on the spectrum difference analysis on multi-spectral images, vegetation indices, terrain factors and tree spices such as moso bamboo, the paper selected TM (1,2,3,4,5,7, etc. bands), vegetation indexs (NDVI, RVI, PVI), elevation, slope and aspect as the independent variable, Y1, Y4, Y6 as the dependent variable,based on the factor screening, stepwise regression is used to construct the index model which can retrieve the hyperspectral characteristics. The models are : where Y1, Y4, Y6 are available classification features for moso bamboo identification in the hyperspectrum, Z6 is the result of Y6 with Box-Cox Transformation, B1, B2, B3, B4 are Band1 to Band4 of TM images, RVI is ratio vegetation index, NDVI is normalized difference vegetation index, Elevation3 is the elevation from 800 to 1200 m, Slight-slope is the slope from 6 to 15°, Abrupt-slope is the slope from 26 to 35°, Steep-slope is the slope from 36 to 45°.
(5) According the index model construction, the hyperspectral characteristics retrieval is experimented. It extracted moso bamboo information with the retrieval results. This classification accuracy is higher than the traditional method based on multi-spectrum data. The overall accuracy based on indices from retrieval is 73.18% with the Kappa of 0.6552, which the accuracy based on original TM data is 62.48% with the Kappa of 0.5170. Compared with them, we can see that the former one is 10.70% and 0.1382 higher than the latter one. Meanwhile, the former moso bamboo accuracy reaches 86.52% and the latter one 74.47%, 12.05% higher. The other species’accuracies also have been raised varying degrees, of which Chinese fir raised 43.44%, masson pine 5.73%, broadleaves 1.51% and economic forest 9.41%.
引文
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