Superpixel-level polarimetric feature extraction based on circular loop spatial pyramid pooling

Abstract

Superpixel-level image classification methods take advantage of contextual information of pixels and reduce the time cost in training and test processes. However, extracting a superpixel-level feature is a challenging task because each superpixel has irregular size and shape. A superpixel-level polarimetric feature extraction (SLPFE) based on circular loop spatial pyramid pooling (CLSPP) (SLPFE_CLSPP) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. The main idea is that the CLSPP layer divides the feature maps of each superpixel into the same number of circular loops and produces consistent feature size for each superpixel, which then feeds into the next processing step, such as classification. SLPFE_CLSPP not only makes it possible to generate the same size feature but also to utilize the spatial information of pixels and reduce the running time during SLPFE process. Compared with the existing methods, the proposed method makes a balance between overall accuracy and running time. Experimental results on real PolSAR dataset demonstrated the superiority of the proposed approach.