Graph Domain Adversarial Network With Dual-Weighted Pseudo-Label Loss for Hyperspectral Image Classification

Abstract

A hyperspectral image (HSI) classification method named graph domain adversarial network with dual-weighted pseudo-label loss (GDAN-DWPL) is proposed in this letter. First, in order to extract more discriminative features, GDAN is applied to the transfer task of HSI. Then, a more reliable spectral–spatial graph is constructed by comprehensively utilizing the abundant spectral features and spatial contextual information. Finally, due to the misalignment of probability distribution on class-level caused by inaccurate pseudo-labels of target domain, a dual-weighted pseudo-label loss is proposed from the perspective of spatiality and confidence. By assigning larger weights to more reliable pixels and eliminating pixels with false pseudo-labels, the negative impact on learning process of prediction model can be reduced. Experimental results on four real HSI datasets show the superiority of GDAN-DWPL.