Dynamic semantic-geometric guidance and structure transfer network for cross-scene hyperspectral image classification

Abstract

Recently, cross-scene hyperspectral image classification(HSIC) via domain adaptation is drawing increasing attention. However, most existing methods either directly align the source domain and target domain without fully mining of SD information, or perform the domain adaptation from semantic and structure aspects with simply characterization method which is sensitive to noise, resulting in the negative transfer and performance decline. To address these issues, in this paper, we propose a novel Dynamic Semantic-Geometric Guidance and Structure Transfer (DSGG-ST) network for cross-scene hyperspectral image classification task. The main aspects of DSGG-ST are twofold. On the one hand, the dynamic semantic-geometric guidance (DSGG) module is designed which consists of the semantic guidance component and geometric guidance component. The proposed DSGG module can align source and target domains under the dynamical guidance of the domain-invariance learning from the semantic and geometric perspectives. On the other hand, the graph attention learning-matching (GALM) module is developed for effectively transferring the structure information between the source domain and target domain. In this module, the graph attention network is adopted to encode the underlying complex structures, and the SeedGNN is exploited for efficient graph matching and alignment. Extensive experiments on three commonly used cross-scene HSI datasets demonstrate that the proposed DSGG-ST obtains a new SOTA performance on cross-scene HSIC, verifying the effectiveness of the proposed DSGG-ST.