Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 113-119, 2012
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B7/113/2012/
doi:10.5194/isprsarchives-XXXIX-B7-113-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
31 Jul 2012
Robust Metric based Anomaly Detection in Kernel Feature Space
B. Du1,2, L. Zhang2, and H. Xin2 1School of Computer Science, Wuhan University, China
2The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing Wuhan University, P.R. China
Keywords: Anomaly detection, hyperspectral images, Manhanlobis distance Abstract. This thesis analyzes the anomalous measurement metric in high dimension feature space, where it is supposed the Gaussian assumption for state-of-art mahanlanobis algorithms is reasonable. The realization of the detector in high dimension feature space is by kernel trick. Besides, the masking and swamping effect is further inhibited by an iterative approach in the feature space. The proposed robust metric based anomaly detection presents promising performance in hyperspectral remote sensing images: the separability between anomalies and background is enlarged; background statistics is more concentrated, and immune to the contamination by anomalies.
Conference paper (PDF, 599 KB)


Citation: Du, B., Zhang, L., and Xin, H.: Robust Metric based Anomaly Detection in Kernel Feature Space, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 113-119, doi:10.5194/isprsarchives-XXXIX-B7-113-2012, 2012.

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