A CFAR algorithm for anomaly detection and discrimination in Hyperspectral Images
| Autor: | Mireille Guillaume, Alexis Huck |
|---|---|
| Přispěvatelé: | Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: |
0211 other engineering and technologies
02 engineering and technology 01 natural sciences Constant false alarm rate 010104 statistics & probability [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Histogram I.5.4 Signal Processing 0101 mathematics 021101 geological & geomatics engineering Mathematics business.industry target detection Hyperspectral imaging Pattern recognition CFAR Object detection ComputingMethodologies_PATTERNRECOGNITION hyperspectral FastICA A priori and a posteriori Algorithm design Anomaly detection Artificial intelligence business Algorithm [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | Proceedings of IEEE International conference on Image Processing IEEE International Conference on Image Processing October 12-15, ICIP 2008 IEEE International Conference on Image Processing October 12-15, ICIP 2008, Oct 2008, San Diego, United States. pp.1868-1871, ⟨10.1109/ICIP.2008.4712143⟩ ICIP |
| DOI: | 10.1109/ICIP.2008.4712143⟩ |
| Popis: | International audience; This paper proposes an anomaly detection algorithm for hyperspectral images. It is unsupervised (the researched spectra are not required a priori), discriminates the anomalies according to their spectra and has a Constant False Alarm Rate (CFAR). The main specificity of this algorithm is to combine these three assets rather than make a tradeoff which is generally necessary with existing methods. It is based on a physically convenient probabilistic model of the FastICA generated independent components. We compare it with the Adaptive Cosine/Coherence Estimator (a reference supervised target detection algorithm) on a real HYDICE dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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