Effects of BOW Model With Affinity Propagation and Spatial Pyramid Matching on Polarimetric SAR Image Classification

The bag of visual words (BOW) model has attracted a great deal of attention in high-resolution remote sensing image classification. Related studies based on polarimetric synthetic aperture radar (PolSAR) image classification are also still challenging. This paper presents anew method of PolSAR image classification based on the BOW model and supplemented by an affinity propagation clustering algorithm (AP), as well as a spatial pyramid matching (SPM) technique (AP_SPM_BOW). Its classification effect is analyzed compared to those of BOW models with other clustering algorithms (e.g., K-means, meanshift, Gaussian mixture model), as well as the ML classification method by using two types of PolSAR images with different resolutions collected from EMISAR and RADARSAT-2 sensors. These results demonstrate that the proposed AP_SPM_BOW model is not only a stable and effective tool for PolSAR image classification but also more suitable for the analysis of high-resolution PolSAR images. In addition, the use of polarimetric information in BOW models can effectively improve the classification accuracy.