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为了提高高分辨率必威现金回扣像土地利用分类精度,该文以金沙江下游河谷地带SPOT 5必威现金回扣像350×350像元作为试验区,在ERDAS IMAGINE 9.0和ENVI 4.1软件平台支持下,采用灰度共生矩阵方法提取必威现金回扣像对比度、角二阶矩、熵、同质度等纹理指标辅助必威现金回扣像分类,分析结果表明,相对于传统监督分类方法,基于纹理特征辅助监督分类方法总Kappa系数提高了9.15%,耕地、林地、水域、建设用地、未利用地Kappa系数分别提高了7.60%、6.17%、3.59%、15.74%和2.96%,分类结果面积准确率分别提高了3.38%、13.47%、4.65%、10.22%和1.53%,说明纹理辅助监督分类方法相对于传统监督分类方法有效提高了土地利用分类精度。 更多还原

【Abstract】 In order to improve land use classification accuracy of high resolution remote sensing images,in this paper,in support of software of ERDAS IMAGINE 9.0 and ENVI 4.1,some texture indexes of SPOT5 image of downstream region of Jinsha River,such as contrast,angular second moment,entropy and homogeneity had been extracted using the gray level co-occurrence matrix.And these texture indexes had been used to assist supervised classification.The results showed that,compared with the method of traditiona...