Cross-Scene Hyperspectral Image Classification Based on Graph Alignment and Distribution Alignment

Abstract

A domain alignment-based hyperspectral image (HSI) classification method was designed to address the heterogeneity in resolution and band between the source domain and target domain datasets of cross-scene hyperspectral images, as well as the resulting reduction in common features. Firstly, after preliminary feature extraction, perform two domain alignment operations: image alignment and distribution alignment. Image alignment aims to align hyperspectral images of different bands or time points, ensuring that they are within the same spatial reference framework. Distribution alignment adjusts the distribution of features of samples of different categories in the feature space to reduce the distribution differences of the same type of features between two domains. Secondly, adjust the consistency of the two alignment methods to ensure that the features obtained through different alignment methods exhibit consistency in the feature space, thereby improving the comparability and reliability of the features. In addition, this method considers multiple losses in the model from different perspectives and makes comprehensive adjustments through a unified optimization process to more comprehensively capture and utilize the correlation information between data. Experimental results on Houston 2013 and Houston 2018 datasets can improve the hyperspectral prediction performance between datasets with different resolutions and bands, effectively solving the problems of high cost and limited training samples in HSI labeling and significantly improving cross-scene HSI classification performance.