Classification of SAR Images in the Presence of Speckle
Su Fu · Systems engineering and electronics · 2002
In this paper, we investigate the effect of speckle reduction on the classification of SAR images. The adaptive Kuan filter and wavelet soft-thresholding filter are respectively used in speckle reduction. The feature vector is composed of tone of image and four texture features based on the gray-level co-occurrence matrix (GLCM). The maximum likelihood classifier is used in image classification. The classification accuracy of the filtered images is compared with that of the unfiltered images. The results show that although the quality of the image improves, the classification accuracy increases slightly after speckle reduction, and even decreases in some cases. This is due to the loss of some structural information in the course of filtering. Accordingly, we propose an improved feature extraction scheme, adopting the tone of filtered image combined with the texture features based on the GLCM of unfiltered image to form the feature vector. The experimental results show that the improved scheme can enhance the performance of classification.