SAR Image Segmentation Based on Complicated Region-Sensitive Adaptive Superpixel Generation and Hybrid Edge Correction

Abstract

Superpixel segmentation algorithms are predominently based on simple linear iterative clustering (SLIC), and treat homogeneous and complex regions equally. This can lead to suboptimal segmentation results, especially in complex images with multiple objects. We address this problem by proposing an SAR image segmentation algorithm based on complicated region-sensitive adaptive superpixel generation and hybrid edge correction (RSASGEC). First, a dynamic initialization algorithm for superpixel seeds based on region complexity is designed. Specifically, a new superpixel representation structure for superpixel seeds is constructed by combining superpixel complexity and the number of contained pixels. The algorithm gives priority to regions with high complexity, dynamically selecting the region with the highest complexity for further partitioning. This results in a dense distribution of superpixel seeds in complex regions, and sparse distributions in homogeneous regions with low complexity. Second, an iterative superpixel segmentation process based on an adaptive energy function is proposed. The Lagrange multiplier mathematical strategy is employed to optimize the adaptive energy function within an adjustable search window, resulting in more compact superpixel segmentation. Finally, a label correction method, based on edge mixture model constraints, is proposed for postprocessing. By integrating edge information from the Gaussian edge detector and the Canny algorithm as constraints, this method leverages majority voting and region growth methods to mitigate edge noise and outliers, refining the superpixel labels. The RSASGEC algorithm is verified in experiments, using one simulated image and six real SAR images. The results indicate that RSASGEC outperforms six representative algorithms, achieving more satisfactory segmentation performance.