基于多尺度纹理和光谱信息的海岛土地利用遥感分类方法研究

本文基于灰度共生矩阵提取多尺度纹理指数影像,将纹理指数影像作为逻辑波段与原始影像波段组合,进行最大似然监督分类的实验,以改善单纯基于像元光谱影像分类技术的不足。利用SPOT-5卫星遥感影像对西门岛土地利用分类的实例进行分析。结果表明,由SPOT-5遥感影像第一波段和第二波段计算所得的相关性和同质性纹理指数影像以及由第三波段计算所得的均值纹理和第四波段计算的同质性纹理指数影像,对海岛土地利用分类精度的提高均有较为显著的效果,其中加入多尺度纹理信息后西门岛土地利用遥感影像分类的总体精度由75.41%提高到89.41%。

Study on island land use classification method based on multi-scale texture and spectral information

Based on gray level co-occurrence matrix(GLCM), different multi-scale texture images were extracted, and then the texture bands were combined with original bands to get new composited multispectral bands. The maximum likelihood supervised classification method was applied to new different composited images to test the differences of classification accuracy. Taking Ximen Island as a case study, through analyzing the differences of classification accuracy of different composited images, the following preliminary conclusions were got: (1)the total classification accuracy can be improved from 75.41% to 89.41% by using multi-scale texture images; (2)among different texture indices, correlation and homogeneity texture image calculated from the first band and second band of SPOT-5 image, mean texture image calculated from the third band, homogeneity texture image calculated from the fourth band can both improve classification accuracy than other texture indices images calculated from other bands.