Polarimetric synthetic aperture radar image unsupervised classification method based on artificial immune system

Abstract

Abstract. An unsupervised classification method based on the H∕α classifier and artificial immune system (AIS) is proposed to overcome the inefficiencies that arise when traditional classification methods deal with polarimetric synthetic aperture radar (PolSAR) data having large numbers of overlapping pixels and excess polarimetric information. The method is composed of two steps. First, Cloude-Pottier decomposition is used to obtain the entropy H and the scattering angle α. The classification result based on the H∕α plane is used to initialize the AIS algorithm. Second, to obtain accurate results, the AIS clonal selection algorithm is used to perform an iterative calculation. As a self-organizing, self-recognizing, and self-optimizing algorithm, the AIS is able to obtain a global optimal solution and better classification results by making use of both the scattering mechanism of ground features and polarimetric scattering characteristics. The effectiveness and feasibility of this method are demonstrated by experiments using a NASA-JPL PolSAR image and a high-resolution PolSAR image of Lingshui autonomous county in Hainan Province.

Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques

“…This information is a key at the continental and global scales for making sure food security, however can also impact the market prices of major staple crops, and even affect forecasts on water-carbon balances and climate dynamics. Within the RS methods, polarimetric SAR (PolSAR) (Schumann et al, 2018), is an advanced RS method (Jie, Gang, Teng, Li, & Qin, 2014) that contains various weather in addition to all-time imaging ability, is competent of containing fabulous advantages contrasted with various quantifying techniques in space (Ren, Li, Gao, & Busche, 2017). Therefore, attracts more attention.…”