Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification

Abstract

Superpixel segmentation has been widely used in the field of computer vision. The generations of PolSAR superpixels have also been widely studied for their feasibility and high efficiency. The initial numbers of PolSAR superpixels are usually designed manually by experience, which has a significant impact on the final performance of superpixel segmentation and the subsequent interpretation tasks. Additionally, the effective information of PolSAR superpixels is not fully analyzed and utilized in the generation process. Regarding these issues, a multiobjective evolutionary superpixel segmentation for PolSAR image classification is proposed in this study. It contains two layers, an automatic optimization layer and a fine segmentation layer. Fully considering the similarity information within the superpixels and the difference information among the superpixels simultaneously, the automatic optimization layer can determine the suitable number of superpixels automatically by the multiobjective optimization for PolSAR superpixel segmentation. Considering the difficulty of the search for accurate boundaries of complex ground objects in PolSAR images, the fine segmentation layer can further improve the qualities of superpixels by fully using the boundary information of good-quality superpixels in the evolution process for generating PolSAR superpixels. The experiments on different PolSAR image datasets validate that the proposed approach can automatically generate high-quality superpixels without any prior information.

A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features

“…The network performance is better than that of existing convolutional neural networks based on real or complex numbers. Chu et al [57] proposed a two-layer multi-objective superpixel segmentation network, with one layer used to optimize network parameters and the other layer used to refine segmentation results can achieve excellent segmentation results without obtaining prior information. These studies all demonstrate that the application of deep learning in the field of PolSAR is very successful.…”