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A Lightweight Hyperspectral Image Anomaly Detector for Real-Time Mission

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China
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Remote Sens. 2019, 11(13), 1622; https://doi.org/10.3390/rs11131622
Received: 29 May 2019 / Revised: 1 July 2019 / Accepted: 3 July 2019 / Published: 8 July 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

In real-time onboard hyperspectral-image(HSI) anomalous targets detection, processing speed and accuracy are equivalently desirable which is hard to satisfy at the same time. To improve detection accuracy, deep learning based HSI anomaly detectors (ADs) are widely studied. However, their large scale network results in a massive computational burden. In this paper, to improve the detection throughput without sacrificing the accuracy, a pruning–quantization–anomaly–detector (P-Q-AD) is proposed by building an underlying constraint formulation to make a trade-off between accuracy and throughput. To solve this formulation, multi-objective optimization with nondominated sorting genetic algorithm II (NSGA-II) is employed to shrink the network. As a result, the redundant neurons are removed. A mixed precision network is implemented with a delicate customized fixed-point data expression to further improve the efficiency. In the experiments, the proposed P-Q-AD is implemented on two real HSI data sets and compared with three types of detectors. The results show that the performance of the proposed approach is no worse than those comparison detectors in terms of the receiver operating characteristic curve (ROC) and area under curve (AUC) value. For the onboard mission, the proposed P-Q-AD reaches over 4.5 × speedup with less than 0.5 % AUC loss compared with the floating-based detector. The pruning and the quantization approach in this paper can be referenced for designing the anomalous targets detectors for high efficiency. View Full-Text
Keywords: hyperspectral image; deep learning; network quantization; real-time processing; multi-objective optimization hyperspectral image; deep learning; network quantization; real-time processing; multi-objective optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ma, N.; Yu, X.; Peng, Y.; Wang, S. A Lightweight Hyperspectral Image Anomaly Detector for Real-Time Mission. Remote Sens. 2019, 11, 1622.

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