Classification of ASTER Image Using SVM and Local Spatial Statistics Gi

摘要：

　　In this paper,the SVM classifier with RBF kernel function was utilized to tackle the classification of ASTER remote sensing image.Instead of the original image,the image of Gi,which is a statistics describing the local spatial structure,is inputted to the SVM classifier to get the final classification result.The classifying process includes a “probing stage and a “classifying stage.The objective of the “probing stage is to find an optimal lag value of Gi;and in the “classifying stage,the Gi image with the optimal lag is classified by the SVM classifier.The experimental result shows that Gi images with appropriate lag values can be used to distinguish land covering features with similar spectral characteristics and different local spatial structures and,as a result,to improve the overall classification accuracy.

关键词： Remote Sensing ASTER SVM local spatial statistics

作者: Xinming Wang Xin Chen

作者单位: Science and Technology on Information Systems Engineering Laboratory Nanjing, China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing, C

会议类型: 国际会议

会议名称: 2012年遥感中的计算机视觉国际会议

会议地点: 厦门

会议语种:英文

页码: 1-5

在线出版日期: 2012-12-16（万方平台首次上网日期，不代表论文的发表时间）