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 this https URL
for the sake of reproducibility.

通过引入深度特征嵌入，提出了一种新颖的超光谱内在图像分解方法（HyperDID），该方法结合环境特征模块和分类特征模块来提取超光谱图像的内在特征，并引入特征判别模块来区分与环境和类别相关的特征，实验证实了 HyperDID 在改进超光谱图像分类性能方面的有效性。此方法的实施可以在此 URL 获得，便于复现。