Abstract
Most of the existing anomaly detection (AD) approaches for hyperspectral images (HSIs) usually achieve high detection accuracy at the cost of high computational complexity. To meet the needs of actual detection scenarios (efficiency, robustness, and accuracy), this letter introduces a fast and robust AD algorithm via subfeatures grouping and binary accumulation (SFBA) for HSIs. We propose a spatial–spectral anomaly scoring strategy to improve detection accuracy. A number of spectral subfeatures of HSI are selected and divided into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> groups as the detection input information and the proposed scoring strategy is carried out for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> group data, respectively, to improve the detection efficiency. Then binary accumulation is introduced to improve the robustness of AD algorithm by accumulating detection results of each group. Our proposed detection algorithm was compared with the existing algorithms on real hyperspectral datasets, thereby verifying its strong robustness and low computational complexity simultaneously.
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