HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding

• URL: http://arxiv.org/abs/2311.14899v1
• Date: Sat, 25 Nov 2023 02:05:10 GMT
• Title: HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
• Authors: Zhiqiang Gong and Xian Zhou and Wen Yao and Xiaohu Zheng and Ping Zhong
• Abstract summary: This study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance.
• Score: 9.32185717565188
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate classification outcomes. However, the classification performance of HIID is constrained by the model's representational ability. To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a Feature Discrimination Module (FDM) is introduced to separate environment-related and category-related features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance. This novel approach holds promise for advancing the capabilities of hyperspectral image analysis by leveraging deep feature embedding principles. The implementation of the proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID for the sake of reproducibility.

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