A hyperspectral anomaly detector based on partitioning pixel into adjacent components

Edisanter Lo

doi:10.1117/12.2017911

18 May 2013 A hyperspectral anomaly detector based on partitioning pixel into adjacent components

Edisanter Lo

Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 874303 (2013) https://doi.org/10.1117/12.2017911
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Detection of anomalous objects in a large scene is an important application of hyperspectral imaging in remote sensing. Current algorithms for anomaly detection are based on partialling out the main background structure from each spectral component of a pixel from a hyperspectral image. The Maximized Subspace Model (MSM) detector has the best probability of detection in comparison with the other anomaly detectors that are based on this model. This paper proposes an anomaly detection algorithm that is based on a more general model than the MSM detector. The anomaly detector is also defined as the Mahalanobis distance of the resulting residual. Experimental results show that the anomaly detector has a substantial improvement in detection over the conventional anomaly detectors.

Citation Download Citation

Edisanter Lo "A hyperspectral anomaly detector based on partitioning pixel into adjacent components", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 874303 (18 May 2013); https://doi.org/10.1117/12.2017911

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