Adaptive Local Discriminant Analysis and Distribution Matching for Domain Adaptation in Hyperspectral Image Classification
Yujie Ning, Jiangtao Peng, Lin Sun, Yi Huang, Weiwei Sun, Qian Du
Abstract
Multimodally distributed data is very common in remote sensing images, such as hyperspectral images (HSIs). It is important to capture the local manifold structure while preserving the global discriminant information in the multimodal data. In this paper, an adaptive local discriminant analysis and distribution matching (ALDADM) method is designed for the domain adaptation (DA) in HSI classification. ALDADM uses adaptive local discriminant analysis to extract features that are discriminative and robust to multimodally distributed data. Meanwhile, considering the spectral properties of HSI, the domain shift is reduced by distribution matching and subspace alignment. During this learning process, ALDADM only selects density peak samples to reduce the influence of interfering samples. Four DA tasks are designing using University of Pavia, Center of Pavia, Yancheng, and Botswana HSI data sets. Compared with existing DA methods, the experimental results demonstrate that our ALDADM offers superior performance.