Superpixel Generating Algorithm Based on Pixel Intensity and Location Similarity for SAR Image Classification

Abstract

Since superpixel takes spatial relationship between pixels into account, which makes the image classification process more understandable and the results more satisfactory, superpixel-based classification methods have been widely studied in recent years. However, due to speckle noise, traditional superpixel generating algorithms still have some drawbacks for synthetic aperture radar (SAR) image. In this letter, we propose a novel superpixel generating algorithm based on pixel intensity and location similarity (PILS) for SAR image. In addition, for the sake of image classification, features of Gabor filters and gray level co-occurrence matrix (GLCM) are extracted from each superpixel. The proposed superpixel generating method has the following three characteristics: (1) the terrain boundaries of SAR image are preserved well; (2) the method has more robustness against speckle noise; and (3) it has high computational efficiency. Experiments on synthetic and real SAR images demonstrate that our method significantly outperforms several state-of-the-art superpixel methods and PILS superpixel-based classification obtains better results than other pixel-based methods.