An effective bag-of-visual-words framework for SAR image classification

The difficulty existing in synthetic aperture radar (SAR) image classification is large amounts of unpredictable and inestimable speckle, leading to degradation of the image quality and concealing important objectives of interest. By exploiting an efficient image features extraction technique, bag-of-visual-words (BOV) for its ability of 'midlevel' feature representation, and a new developed non-local (NL-) means denosing method suitable for multiplicative speckle, we present a novel and effective BOV framework for SAR image classification. Compared with the other two representative algorithms, the experimental results show that the proposed algorithm has obtained more satisfactory and cogent classification performance and performed more robustness to SAR speckle.