Abstract

Hyperspectral anomaly detection aims to classify the anomalous objects in the scene. However, the spatial resolution of the hyperspectral images is relatively low, leading to inaccurate detection of abnormal pixels. Existing methods either ignore the low-resolution problem or leverage super-resolution models to reconstruct the global image to detect abnormal pixels. We claim that reconstructing super-resolution of the global image is unnecessary, while the area where the abnormal target is located should be paid more attention to be reconstructed. In this paper, we propose a super-resolution reconstruction with an attention mechanism for hyperspectral anomaly detection. Our method can automatically extract additional high-frequency information from low-spatial-resolution images and detect abnormal pixels simultaneously. Furthermore, the spatial-channel attention mechanism is adopted to select significant features for reconstructing super-resolution images by assigning different weights to different channels and different spatial–spectral locations. Finally, a regularized join loss function is proposed that balances different tasks by adjusting the relative weight. The experimental results on the public hyperspectral real datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

© 2021 Optical Society of America

Hyperspectral deep convolution anomaly detection based on weight adjustment strategy

Dan Chong, Bingliang Hu, Xiaohui Gao, Hao Gao, Pu Xia, and Yinhua Wu
Appl. Opt. 59(31) 9633-9642 (2020)

Super-resolution reconstruction of terahertz images based on a deep-learning network with a residual channel attention mechanism

Xiuwei Yang, Dehai Zhang, Zhongmin Wang, Yanbo Zhang, Jun Wu, Biyuan Wu, and Xiaohu Wu
Appl. Opt. 61(12) 3363-3370 (2022)

Super-resolution reconstruction of underwater polarized images with a fused attention mechanism

Liying Lang, Haoyi Feng, Jinghan Zhang, and Yajun Pang
Appl. Opt. 63(6) 1590-1599 (2024)

Hyperspectral image reconstruction via patch attention driven network

Yechuan Qiu, Shengjie Zhao, Xu Ma, Tong Zhang, and Gonzalo R. Arce
Opt. Express 31(12) 20221-20236 (2023)

Tensor decomposition-based sparsity divergence index for hyperspectral anomaly detection

Lili Zhang and Chunhui Zhao
J. Opt. Soc. Am. A 34(9) 1585-1594 (2017)