A feature combining spatial and structural information for SAR image classification

In this paper, we propose a theoretically new and effective feature for SAR image classification. The new feature combines traditional gray level co-occurrence matrix (GLCM) textural feature and the recent multilevel local pattern histogram (MLPH) feature. It can not only describe intrinsic property of land-cover/land-use surfaces, corresponding to textural information, but it also captures both local and global structural information. Experiments on real SAR images demonstrate that the proposed feature obtains better results than the original GLCM and MLPH features in SAR image classification.