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A robust background regression based score estimation algorithm for hyperspectral anomaly detection / Zhao Rui in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 122 (December 2016) . - pp 126 – 144 Titre : A robust background regression based score estimation algorithm for hyperspectral anomaly detection Type de document : Article/Communication Auteurs : Zhao Rui, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur ; Lefei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 126 – 144 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'anomalie [Termes IGN] image hyperspectrale [Termes IGN] régression Résumé : (Auteur) Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement in practice. Numéro de notice : A2016--023 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.10.006 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.10.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83886 [article]