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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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 109; no. 8; pp. 1306 - 1319
Main Authors: Du, Qian, Tang, Bo, Xie, Weiying, Li, Wei
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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 stress surveying, and law enforcement-related uses, where prior information of targets is often unavailable. The key to success is accurate background modeling. Anomaly detection from remote sensing images is challenging because spatial coverage is very large and the background is highly heterogeneous. For pixel-based anomaly detection, computing cost in background modeling and a spatial-convolution-type detection process is very expensive. Thus, parallel and distributed computing is critical in reducing execution time, which can fit the need for real-time or near real-time detection from airborne and spaceborne platforms in support of immediate decision-making. This article reviews the recent advances in anomaly detection from hyperspectral remote sensing images and their implementation using parallel and distributed systems. The classical methods, i.e., the Reed-Xiaoli (RX) algorithm and its variants, including its real-time processing version, are illustrated in commodity graphic processing units (GPUs), cloud, and field-programmable gate array (FPGA) implementations. Practical issues and future development trends are also discussed.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2021.3076455