Zhaokui Li
ABSTRACT
With the increasing number of hyperspectral remote sensing images, it is of great research significance to utilize labeled source domain images to achieve accurate classification of unlabeled target domain images quickly. However, due to variations in atmospheric conditions, lighting conditions, temporal factors, and spatial morphologies of materials within the same object category, the spectral shift frequently occurs, leading to significant data distribution differences between the source and target domains. This phenomenon hinders the adaptability of the classification model trained on source domain data to the target domain data, thereby seriously affecting the effectiveness of domain adaptation methods. To address the issue of significant distribution differences and lack of labels between domains, this paper proposes a hyperspectral image classification method based on attention subdomain adaptation(ASDA). Firstly, a spatial-spectral feature extractor is used to extract features, aiming to make full use of the information in HSI. Then, a attention-based conditional distribution alignment method is proposed to align the features of interest, which can alleviate the negative impact of redundant pixels and redundant bands on subdomain alignment. Specifically, spatial attention is used to remove the influence of spatial interference pixels, while channel attention is employed to eliminate redundant bands in the spectrum. Finally, the subdomains are aligned using the Local Maximum Mean Discrepancy (LMMD) criterion, which focuses on aligning more effective features of the attention network. This alignment enables accurate classification of the target domain data. The effectiveness of this method is validated on two datasets.
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Index Terms
- Attention-Based Subdomain Adaptation Network for Hyperspectral Image Classification
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