HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature
  Embedding
Zhiqiang Gong Xian Zhou Wen Yao Xiaohu Zheng Ping Zhong
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.