摘要
为弥补光学遥感在煤矿区资源与生态环境监测中应用的不足,研究应用全极化SAR图像有效提取煤矿区土地覆盖信息的方法具有重要意义。针对全极化SAR图像极化信息丰富、斑点噪声多、局部异质性大等特点,提出采用面向对象的影像分类方法对其进行分层土地覆盖信息提取。以徐州市西南部的煤矿区为研究区,选取Radarsat-2的全极化SAR图像,分析了研究区内全极化SAR图像中典型地物的灰度特征,提出面向对象分类方法所涉及的最优分割尺度选择法,给出全极化SAR图像分割对象后向散射特征的计算方法。对研究区的SAR图像进行试验,首先对SAR图像进行多尺度分割,选择各土地覆盖类型的最优分割尺度,然后在该尺度下计算出土地覆盖类型的后向散射特征指数,最后采用模糊逻辑分类法分层提取出研究区内的土地覆盖信息。结果表明:在适于各土地覆盖类型提取的最优分割尺度下,充分利用分割对象的灰度、形状、纹理以及类间相关特征,并综合应用隶属函数法和最邻近分类法,能有效地提取煤矿区的农田、道路、塌陷地、建筑物、山林这5类土地覆盖信息。与最大似然分类法相比,该方法能够较好地消除"椒盐现象",各种土地覆盖类型的提取精度都有所提高,其总体分类精度可提高38.3%。
An effective approach of extracting land cover information from fully polarimetric SAR images has been compensating for the lack of optical remote sensing data.This approach has also promoted the application of SAR technology in resources and ecological monitoring in coal mining areas.With abundant polarimetric information,speckle and large local heterogeneity of fully polarimetric SAR images,this study proposed an object-oriented image classification approach for hierarchical extraction of land cover information.The study area was a coal mining region in the southwest of Xuzhou City and the SAR image acquired by Radarsat-2 for a fully polarimetric image.The gray features of typical surface objects of SAR images for the study area were analyzed.The optimal segmentation scale for object-oriented image classification was proposed.The computation approach of backscatter features of segmentation objects was also presented.Also ground-truth data were collected on the land cover to verify the SAR image for the study area.In the first step,the SAR image was segmented into several scales and the optimal segmentation scale of each land cover type selected.In the second step,the backscatter feature indexes of each land cover type were calculated under the optimal segmentation scale.Then in the last step,the land cover information in the study area was hierarchically extracted using the fuzzy logic classification method.The results showed that the proposed approach accurately extracted the five types of land cover information under the optimal segmentation scales of land cover type,including farmland,road,subsidence land,building and mountain woodland.In terms of gray,shape,texture and class-related features of the segmentation objects,the membership function and nearest neighbor classification approaches weakened the ＂pepper phenomenon＂.Compared with the maximum likelihood classification method,the classification accuracy of each land cover type improved while the average classification accuracy improved by 38.3%.In pa
出处
《中国生态农业学报》
CAS
CSCD
北大核心
2013年第9期1142-1148,共7页
Chinese Journal of Eco-Agriculture
基金
国家自然科学基金项目(41101428)
现代工程测量国家测绘局重点实验室开放课题(TJES1204)资助
关键词
全极化SAR图像
面向对象
多尺度分割
分类精度
土地覆盖
煤矿区
Fully polarimetric SAR image
Object-oriented method
Multiple-scale segmentation
Classification accuracy
Land cover
Coal mining area
