摘要
SAR图像上水体和居民地信息的提取在实际应用中具有重要的意义。为了更好地提取SAR图像上水体和居民地,以单波段单极化Radarsat-1 SAR图像为研究对象,首先利用半变异函数分析样本图像的结构特性来确定纹理信息提取的最佳参数;然后,在此基础上基于灰度共生矩阵计算SAR图像均值、角二阶矩和熵3种纹理测度,建立了适于图像分类的多维特征空间,从而有效地增强了水体和居民地信息;最后通过样本采集,使用支持向量机分类器进行水体和居民地信息提取,并采用近期归一化植被指数(NDVI)数据和分类结果进行目标层融合来消除山体因素的影响,信息提取的结果显示,分类总体精度为82.57%,Kappa系数为0.58,较准确地提取了水体和居民地信息。
It is very important to extract water and settlement areas from SAR images in practical applications. In this paper the single band and single-polarization Radarsat-1 SAR image is used to water and settlement area extraction. Firstly, the statistic structure information of sample image is analyzed using semi-variogram to determine the optimum parameters for textural information extraction. In order to establish the multi-dimenslon feature space for image classification, the textural measures such as mean, angle second moment and entropy have been calculated based on grey level co-occurrence matrix method. Then the water and settlement area information can be enhanced effectively using false color composite method. Three types of sample such as water, settlement and other are collected as training samples, and the image data are processed using support vector machine classification method. Finally, the image fusion on the target level between classification result image and NDVI image is conducted in order to eliminate the mountain influence, and the water and settlement areas are extracted accurately with a total classification accuracy of 82.57% , and Kappa coefficient of 0.58.
出处
《中国图象图形学报》
CSCD
北大核心
2008年第2期257-263,共7页
Journal of Image and Graphics
基金
国家自然科学基金项目(40671130)
关键词
合成孔径雷达
半变异函数图
灰度共生矩阵
支持向量机
synthetic aperture radar（SAR）, semi-variogram, grey level co-occurrence matrix, support vector machine （ SVM ）
