Blind-block reconstruction network with a guard window for hyperspectral anomaly detection

Degang Wang; Lina Zhuang; Lianru Gao; Xu Sun; Ye Liu

doi:10.1117/12.3010359

15 November 2023 Blind-block reconstruction network with a guard window for hyperspectral anomaly detection

Degang Wang, Lina Zhuang, Lianru Gao, Xu Sun, Ye Liu

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128152E (2023) https://doi.org/10.1117/12.3010359
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Most existing depth networks that perform hyperspectral anomaly detection (HAD) using reconstruction errors tend to fit anomalous pixels, resulting in small reconstruction errors for anomalies, which are not favorable for separating targets from hyperspectral images (HSIs). To achieve a superior background reconstruction network for HAD purposes, a self-supervised blind-block reconstruction network (termed BockNet) with a guard window is proposed. BockNet creates a blind-block (guard window) at the center of the network's receptive field, making it unable to see the information inside the guard window when reconstructing the central pixel. This process seamlessly embeds a sliding dual-window model into our BockNet, where the inner window is the guard window, and the outer window is the receptive field outside the guard window. Naturally, BockNet uses only the outer window information to predict/reconstruct the central pixel of the perceptive field. During the reconstruction of pixels inside anomalous targets of different sizes, the targets typically fall into the guard window, weakening the contribution of anomalies to the reconstruction results and allowing these reconstructed pixels to converge to the background distribution of the outer window region. Accordingly, the reconstructed HSI can be regarded as a pure background HSI, and the reconstruction error of anomalous pixels will be further enlarged, thus improving the discrimination ability of the proposed network for anomalies. Extensive experiments on two datasets show the competitive performance of our BockNet compared to state-of-the-art detectors.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Degang Wang, Lina Zhuang, Lianru Gao, Xu Sun, and Ye Liu "Blind-block reconstruction network with a guard window for hyperspectral anomaly detection", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128152E (15 November 2023); https://doi.org/10.1117/12.3010359

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KEYWORDS

Image restoration

Target detection

Data modeling

Convolution

Deep learning

Machine learning

Deep convolutional neural networks

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