Deep Neural Networks have been successfully applied in hyperspectral image
classification. However, most of prior works adopt general deep architectures
while ignore the intrinsic structure of the hyperspectral image, such as the
physical noise generation. This would make these deep models unable to generate
discriminative features and provide impressive classification performance. To
leverage such intrinsic information, this work develops a novel deep learning
framework with the noise inclined module and denoise framework for
hyperspectral image classification. First, we model the spectral signature of
hyperspectral image with the physical noise model to describe the high
intraclass variance of each class and great overlapping between different
classes in the image. Then, a noise inclined module is developed to capture the
physical noise within each object and a denoise framework is then followed to
remove such noise from the object. Finally, the CNN with noise inclined module
and the denoise framework is developed to obtain discriminative features and
provides good classification performance of hyperspectral image. Experiments
are conducted over two commonly used real-world datasets and the experimental
results show the effectiveness of the proposed method. The implementation of
the proposed method and other compared methods could be accessed at
https://github.com/shendu-sw/noise-physical-framework