{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T22:27:58Z","timestamp":1791412078581,"version":"4.3.3"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["HIT.NSRIF.201615"],"award-info":[{"award-number":["HIT.NSRIF.201615"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010255","name":"Guangxi Key Laboratory of Automatic Detecting Technology and Instruments","doi-asserted-by":"publisher","award":["YQ15201"],"award-info":[{"award-number":["YQ15201"]}],"id":[{"id":"10.13039\/501100010255","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571160"],"award-info":[{"award-number":["61571160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013quantization\u2013anomaly\u2013detector (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 \u00d7     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.<\/jats:p>","DOI":"10.3390\/rs11131622","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T11:02:37Z","timestamp":1562583757000},"page":"1622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Lightweight Hyperspectral Image Anomaly Detector for Real-Time Mission"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9684-001X","authenticated-orcid":false,"given":"Ning","family":"Ma","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ximing","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compag.2014.08.009","article-title":"Hyperspectral band selection for detecting different blueberry fruit maturity stages","volume":"109","author":"Yang","year":"2014","journal-title":"Comput. 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