Robust high-order matched filter for hyperspectral target detection

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Robust high-order matched filter for hyperspectral target detection

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A robust high-order matched filter (RHMF) for automatic target detection in hyperspectral images is proposed. The classical detection methods mainly focus on second-order statistics and do not take intrinsic uncertainty or variability of target spectral signatures into account. For automatic target detection in a hyperspectral image, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Also, one difficult point in target detection is the inherent variability in target spectral signatures. Under such circumstances, the RHMF algorithm uses high-order statistics, and takes variability into consideration, and has been shown by presented experiments to be more effective than classical detection methods.

Inspec keywords: object detection; higher order statistics; matched filters; geophysical image processing

Other keywords: high-order statistics; robust high-order matched filter; second-order statistics; automatic target detection; hyperspectral images

Subjects: Probability theory, stochastic processes, and statistics; Geography and cartography computing; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Optical, image and video signal processing; Filtering methods in signal processing; Other topics in statistics; Other topics in statistics; Data and information; acquisition, processing, storage and dissemination in geophysics; Geophysical techniques and equipment; Computer vision and image processing techniques

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