Title :
Classification of SAR image based on gray cooccurrence matrix and support vector machine
Author :
Su Fulin ; Ni Liang ; Dafang, Li ; Huadong, Sun
Author_Institution :
Harbin Inst. of Technol., China
fDate :
31 Aug.-4 Sept. 2004
Abstract :
In this paper, the classification method of SAR image based on support vector machine (SVM) with extracting the gray feature, texture feature (gray cooccurrence matrix) was proposed. Compared the results of different kernel functions with the result of maximum likelihood classifier, this approach was proved to be able to classify those patterns that can´t be distinguished exactly by the maximum likelihood classifier. On the other hand, experimental results showed the classification precision of SVM with linear kernel function is higher than that with Gauss kernel function.
Keywords :
feature extraction; image classification; image texture; matrix algebra; radar imaging; synthetic aperture radar; SAR image classification; SVM; gray cooccurrence matrix; gray feature extraction; linear kernel function; maximum likelihood classifier; support vector machine; texture feature; Gaussian processes; Ground penetrating radar; Image classification; Image generation; Kernel; Radar imaging; Sun; Support vector machine classification; Support vector machines; Synthetic aperture radar;
Conference_Titel :
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
Print_ISBN :
0-7803-8406-7
DOI :
10.1109/ICOSP.2004.1441584