MSUANet: Multistage Information Sharing Multitask Hyperspectral Classification Network With Unmixing Assistance

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Abstract
Multitask learning (MTL) can effectively exploit the complementary information across different tasks in hyperspectral images (HSIs) to support classification. However, existing MTL methods for HSI classification usually employ shared structures in the early stages or simple information fusion in the later stages, failing to adequately characterize task-specific feature differences and adaptively select task-relevant information. Therefore, we propose a multi-stage information sharing multitask hyperspectral classification network with unmixing assistance (MSUANet). The proposed method adopts a three stage strategy, including low-level information sharing, task-specific information mining, and adaptive latent information selection, to fully exploit the information gain introduced by the unmixing task and provide more fine-grained spectral details for improved classification performance. In addition, we propose an information diverter with hybrid attention (IDHA) module, which enhances task-specific information for classification and unmixing, respectively. Furthermore, we design the adaptive latent abundance knowledge transfer (ALAKT) module, which adaptively selects latent complementary information for both tasks. Experimental results on Indian Pines, Houston 2013, Berlin, and Washington DC Mall datasets demonstrate that MSUANet outperforms state-of-the-art methods. The source code will be available at https://github.com/chihuajiao/HSIMTL.
Funding Information
  • National Natural Science Foundation of China (62376205)
  • National Natural Science Foundation of China (U25A20528)
  • Natural Science Foundation of Shandong Province (ZR2023LZH018)

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