利用纹理分析方法提取CBERS02星CCD图像土地覆盖信息  

Extracting Land Cover Information From CBERS-2's CCD Image Using Texture Analysis

作　　者：彭光雄[1] 李京[1] 何宇华[2] 胡德勇[1] 

机构地区：[1]北京师范大学资源学院资源技术与工程研究所,北京100875 [2]中国土地勘测规划院,北京100037

出　　处：《遥感技术与应用》2007年第1期8-13,共6页Remote Sensing Technology and Application

基　　金：国家自然科学基金项目(40201036);国产中巴卫星遥感数据应用研究(05-1.10.1)

摘　　要：以河北省廊坊地区为试验区,采用纹理分析方法对CBERS02星CCD5全色波段图像的纹理信息进行了分析。选取灰度共生矩阵的角二阶矩、对比度、熵和相关4个统计量,利用变异系数来选择各个统计量对应的最佳纹理滤波窗口大小。然后利用这4个统计量的纹理图像与CCD1-4波段的多光谱图像共同参与分类。试验结果表明,将纹理分析方法应用于图像分类中可区分光谱混淆的地类,光谱与纹理特征结合得到的分类精度要远高于单纯光谱的分类精度。The maximum likelihood classification （MLC） is one of the most popular methods in remote sensing image classification. Because the maximum likelihood classification is based on spectrum of objects, it cannot correctly distinguish objects that have same spectrum and cannot reach the accuracy requirement. In this paper, we take an area of Langfang of Hebei province as an example and discuss the method of combining texture of panchromatic image with spectrum to improve the accuracy of CBERS02 CCD image information extraction. Firstly, analysis of the textures of the panchromatic image（CCD5） is made by Using texture analysis of Gray Level Coocurrence Matrices and statistic index. Then optimal texture window size of angular second moment, contrast, entropy and correlation is obtained according to variation coefficient of each texture measure for each thematic class. The chosen optimal window size is that from which the value of variation coefficient starts to stabilize while having the smallest value. The output images generated by texture analysis are used as additional bands together with other multi-spectral bands（CCD1-4） in classification. Objects that have same spectrums such as grass land and cultivate land are distinguished. Finally, the accuracy measurement is compared with the classification based on spectrum only . The result indicates that the objects with same spectrum are distinguished by using texture analysis in image classification, and the combination improves more than spectrum only in classification accuracy.

关 键 词：纹理分析 灰度共生矩阵 精度评价 CBERS02星 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]