Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
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...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/4878 |
| _version_ | 1797532599956013056 |
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| author | Ivan Racetin Andrija Krtalić |
| author_facet | Ivan Racetin Andrija Krtalić |
| author_sort | Ivan Racetin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:02:32Z |
| format | Article |
| id | doaj.art-878eabecbadc43b0aa9b486954f38294 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:02:32Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-878eabecbadc43b0aa9b486954f382942023-11-21T21:26:14ZengMDPI AGApplied Sciences2076-34172021-05-011111487810.3390/app11114878Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing ApplicationsIvan Racetin0Andrija Krtalić1Faculty of Civil Engineering, Architecture and Geodesy, University of Split, 21000 Split, CroatiaFaculty of Geodesy, University of Zagreb, 10000 Zagreb, CroatiaHyperspectral 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.https://www.mdpi.com/2076-3417/11/11/4878target detectionReed-Xiaoli algorithmbackground modelskernel-based methodsrepresentation models |
| spellingShingle | Ivan Racetin Andrija Krtalić Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications Applied Sciences target detection Reed-Xiaoli algorithm background models kernel-based methods representation models |
| title | Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications |
| title_full | Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications |
| title_fullStr | Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications |
| title_full_unstemmed | Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications |
| title_short | Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications |
| title_sort | systematic review of anomaly detection in hyperspectral remote sensing applications |
| topic | target detection Reed-Xiaoli algorithm background models kernel-based methods representation models |
| url | https://www.mdpi.com/2076-3417/11/11/4878 |
| work_keys_str_mv | AT ivanracetin systematicreviewofanomalydetectioninhyperspectralremotesensingapplications AT andrijakrtalic systematicreviewofanomalydetectioninhyperspectralremotesensingapplications |