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16 September 2003 Further results in the use of independent component analysis for target detection in hyperspectral images
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Abstract
The paper presents a novel algorithm based on Independent Component Analysis (ICA) for the detection of small targets present in hyperspectral images. Compared to previous approaches, the algorithm provides two significant improvements. First, an important speedup is obtained by preprocessing the data through spectral screening. Spectral screening is a technique that measures the similarity between pixel vectors by calculating the angle between them. For a certain threshold α, a set of pixel vectors is selected such that the angle between any two of them is larger than α and the angle between any of the pixel vectors not selected and at least one selected vector is smaller than α. In addition to significantly reducing the size of the data, spectral screening reduces the influence of dominating features. The second improvement is the modification of the Infomax algorithm such that the number of components that are produced is lower than the number of initial observations. This change eliminates the need for feature reduction through PCA, and leads to increased accuracy of the results. Results obtained by applying the new algorithm on data from the hyperspectral digital imagery collection experiment (HYDICE) show that, compared with previous ICA based target detection algorithms developed by the authors, the novel approach has an increased efficiency, at the same time achieving a considerable speedup. The experiments confirm the efficiency of ICA as an attractive tool for hyperspectral data processing.
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Stefan A. Robila and Pramod Kumar Varshney "Further results in the use of independent component analysis for target detection in hyperspectral images", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); https://doi.org/10.1117/12.487452
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