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Review

Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

1
Faculty of Civil Engineering, Architecture and Geodesy, University of Split, 21000 Split, Croatia
2
Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Academic Editor: Byoung-Kwan Cho
Appl. Sci. 2021, 11(11), 4878; https://doi.org/10.3390/app11114878
Received: 6 May 2021 / Revised: 21 May 2021 / Accepted: 23 May 2021 / Published: 26 May 2021
Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications. View Full-Text
Keywords: target detection; Reed-Xiaoli algorithm; background models; kernel-based methods; representation models target detection; Reed-Xiaoli algorithm; background models; kernel-based methods; representation models
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MDPI and ACS Style

Racetin, I.; Krtalić, A. Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications. Appl. Sci. 2021, 11, 4878. https://doi.org/10.3390/app11114878

AMA Style

Racetin I, Krtalić A. Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications. Applied Sciences. 2021; 11(11):4878. https://doi.org/10.3390/app11114878

Chicago/Turabian Style

Racetin, Ivan, and Andrija Krtalić. 2021. "Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications" Applied Sciences 11, no. 11: 4878. https://doi.org/10.3390/app11114878

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