We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A CNN with noise inclined module and denoise framework for hyperspectral image classification.
- Authors
Gong, Zhiqiang; Zhong, Ping; Yao, Wen; Zhou, Weien; Qi, Jiahao; Hu, Panhe
- 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, the spectral signature of hyperspectral image is modeled with the physical noise model to describe the high intra‐class 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.
- Subjects
DEEP learning; IMAGE recognition (Computer vision); ARTIFICIAL neural networks; NOISE
- Publication
IET Image Processing (Wiley-Blackwell), 2023, Vol 17, Issue 9, p2575
- ISSN
1751-9659
- Publication type
Article
- DOI
10.1049/ipr2.12733