摘要
高空间分辨率遥感图像在土地覆被分类方面应用广泛,但传统的基于像元分类方法的精度较低。为了提高高分辨率图像的分类精度,通过灰度共生矩阵法快速提取纹理特征,利用支持向量机(SVM)并辅以纹理特征,对浙江湖州典型实验样区的ALOS图像进行土地覆被分类。结果表明:基于纹理特征和SVM的图像分类能更好地提取地物信息,分类总精度达到90.88%;单纯SVM的分类精度(89.96%)高于最大似然法(分类精度86.16%)。本文方法可快速准确地提取土地覆被类型,为研究农业非点源污染的产生和时空分布提供服务,进而为寻求太湖流域内合理的土地利用模式和土地的可持续利用提供科学依据。
The high spatial resolution remote sensing images are used widely in the land cover classification;nevertheless,the traditional pixel-based classification has the weakness of relatively low accuracy.For the purpose of improving the accuracy of the high spatial resolution image classification,the textural features were extracted quickly by using the method of Gray Level Co-occurrence Matrices（GLCM）,and then the ALOS image of the typical test area in Huzhou city of Zhejiang province was classified based on textural features and Support Vector Machine（SVM）.The results show that image classification based on textural features and SVM can better extract surface features with precision of 90.88%.The classification precision based on SVM only is higher than that based on maximum likelihood,with the former precision being 89.96% and the latter 86.16%.Extracting land cover types quickly and accurately can provide a service for the research on appearance and spatial-temporal distribution of the agricultural non-point pollution source,and also provide scientific evidence for exploration of reasonable land use model and sustainable land utilization in Taihu basin.
出处
《国土资源遥感》
CSCD
2011年第4期58-63,共6页
Remote Sensing for Land & Resources
基金
国家科技重大专项"水体污染控制与治理"之"太湖流域水生态功能分区与质量目标管理技术示范"项目(编号:2008ZX07526-007)
国家自然科学基金项目(编号:40871230)共同资助
