Texture Feature Extraction Method Combining Nonsubsampled Contour Transformation with Gray Level Co-occurrence Matrix

Journal of Multimedia

关键词: Feature Selection;    Support Vector Machine;    Gray Level Co-Occurrence Matrix;    Nonsubsampled Contour Transformation;    Image Segmentation;    Synthetic Aperture Radar;   

Others  :  1017311
DOI  :  10.4304/jmm.8.6.675-684

【 摘 要 】

Gray level co-occurrence matrix (GLCM) is an important method to extract the image texture features of synthetic aperture radar (SAR). However, GLCM can only extract the textures under single scale and single direction. A kind of texture feature extraction method combining nonsubsampled contour transformation (NSCT) and GLCM is proposed, so as to achieve the extraction of texture features under multi-scale and multi-direction. We firstly conducted multi-scale and multi-direction decomposition on the SAR images with NSCT, secondly extracted the symbiosis amount with GLCM from the obtained sub-band images, then conducted the correlation analysis for the extracted symbiosis amount to remove the redundant characteristic quantity; and combined it with the gray features to constitute the multi-feature vector. Finally, we made full use of the advantages of the support vector machine in the aspects of small sample database and generalization ability, and completed the division of multi-feature vector space by SVM so as to achieve the SAR image segmentation. The results of the experiment showed that the segmentation accuracy rate could be improved and good edge retention effect could be obtained through using the GLCM texture extraction method based on NSCT domain and multi-feature fusion in the SAR image segmentation.

【 授权许可】

   
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