Parallel and Distributed Computing for Anomaly Detection From Hyperspectral Remote Sensing Imagery

Anomaly detection from remote sensing images is to detect pixels whose spectral signatures are different from their background. Anomalies are often man-made targets. With such target signatures being unknown, anomaly detection has many important applications, such as water quality monitoring, crop s...

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Vydáno v:	Proceedings of the IEEE Ročník 109; číslo 8; s. 1306 - 1319
Hlavní autoři:	Du, Qian, Tang, Bo, Xie, Weiying, Li, Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Anomalies Anomaly detection Computer networks Computing costs Convolution Decision making Detectors Distributed computing Distributed databases Distributed processing Feature extraction Field programmable gate arrays field-programmable gate array (FPGA) Graphics processing units Hyperspectral imaging hyperspectral remote sensing large-scale data Modelling parallel computing Parallel processing Pixels Principal component analysis Real time real-time processing Real-time systems Remote sensing Spectral signatures Water quality
ISSN:	0018-9219, 1558-2256
On-line přístup:	Získat plný text
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