Work

A Novel PolSAR Classification Method Based on Dynamic Weight Adjustment of Heterogeneous Feature Fusion

Year: 2026

Type: article

Abstract: In response to the problems of insufficient fusion of amplitude and phase heterogeneity features, deficient direction sensitivity modeling, and a single fusion level in the polarimetric synthetic aperture radar classification task, this paper proposes a PolSAR classification method based on dynamic weight adjustment and heterogeneous feature fusion. This method utilizes a dual-branch parallel structure to extract polarization features and landcover amplitude-phase direction difference features separately and constructs a three-level progressive fusion strategy of sub-branch, cross-branch, and decision layer to achieve adaptive complementation of heterogeneous features. Experiments on three standard datasets show that the classification accuracy and visual consistency of this method are significantly superior to the classical methods, with the overall accuracy being improved by 1.5% to 2.4%.

Source: Remote Sensing

DOI: 10.3390/rs18081140

Authors: Yan Duan, Sonya Coleman, Li Yang, Haijun Wang, Guangwei Wang, Dermot Kerr

Institutions: Hubei University of Education, University of Ulster, Ulster University, Derry-Londonderry Campus

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: China Scholarship Council