EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection

Abstract

The generation of superpixels is becoming a critical step in SAR image segmentation. However, most studies on superpixels only focused on clustering methods without considering multi-feature in SAR images. Generating superpixels for complex scenes is a challenging task. It is also time consuming and inconvenient to manually adjust the parameters to regularize the shapes of superpixels. To address these issues, we propose a new superpixel generation method for SAR images based on edge detection and texture region selection (EDTRS), which takes into account the different features of SAR images. Firstly, a Gaussian function is applied in the neighborhood of each pixel in eight directions, and a Sobel operator is used to determine the redefined region. Then, 2D entropy is introduced to adjust the edge map. Secondly, local outlier factor (LOF) detection is used to eliminate speckle-noise interference in SAR images. We judge whether the texture has periodicity and introduce an edge map to select the appropriate region and extract texture features for the target pixel. A gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) are combined to extract texture features. Finally, we use a novel approach to combine the features extracted, and the pixels are clustered by the K-means method. Experimental results with different SAR images show that the proposed method outperforms existing superpixel generation methods with an increase of 5–10% in accuracy and produces more regular shapes.