A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification

TL;DR

This paper introduces a novel CNN framework for hyperspectral image classification that incorporates a noise inclined module and denoise framework to better handle physical noise and improve discriminative feature extraction.

Contribution

The work develops a new deep learning framework that models physical noise in hyperspectral images and integrates noise removal to enhance classification accuracy.

Findings

Effective noise modeling improves feature discrimination.

Proposed method outperforms existing approaches on real datasets.

Framework demonstrates robustness to intrinsic spectral noise.

Abstract

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.