Heterogeneous Spectral-Spatial Feature Transfer With Structure Preserved Distribution Alignment for Hyperspectral Image Classification

Abstract

Cross-scene knowledge transfer has been proven effective to deal with the small-sample problem in hyperspectral image (HSI) classification. Currently, most of the existing works are based on the homogeneous settings where the source and target HSIs are observed from the same feature space, while the heterogeneous situation, which is more common in real-world applications, is under insufficient exploration. In this article, we propose a novel transfer learning approach for HSI classification, which is capable of transferring discriminative spectral-spatial feature between heterogeneous datasets. Specifically, we first extract spectral-spatial features of source and target scenes by applying the multiscale convolutional sparse decomposition (MCSD) method. By performing MCSD, the spectral information and spatial structure information at different scales can be jointly adapted to learn transferable features for classification. Then, in order to overcome the heterogeneity between the two feature sets, we build a structure preserved distribution alignment (SPDA) model to learn domain-specific projections to map the feature samples into a shared latent subspace where the discriminative knowledge can be effectively transferred. With proper reformulation, we give the analytical solution of the objective function and generate an optimization approach to solve the SPDA model efficiently. Experiments conducted on several real data pairs demonstrate that the proposed approach can explicitly narrow the disparity between heterogeneous HSIs, and yield superior classification results compared with other representative heterogeneous transfer learning methods.