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24 August 2017 Unassisted reduction and segmentation of large hyperspectral image datasets
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The information density in hyperspectral data is not uniform across the spectral and spatial dimensions, and the overall information sparsity is often high. While these non-uniformities underpin the sought-after image contrast, high sparsity generates unnecessarily long acquisition and data processing times. Conventional reduction techniques like those based on principal components analysis (PCA) sacrifice the contributions of minority pixel populations while retaining those representing a greater portion of the overall variability. The effect is that some regions in the reconstructed images achieve a higher degree of recovery than other locations, making it difficult to assess the meaning or relevance of the minority pixels, even when this information would reveal important sample defects or spectral inhomogeneities. In the work presented here, we introduce a novel user-unassisted data reduction and image segmentation method called reduction of spectral images (ROSI). The aim of ROSI is to achieve a threshold information density in the spectral dimension for all image pixels. The result effectively segments the image in a manner that provides rapid image contrast that is comparable to traditionally classified images, but does so without a priori information. In addition, ROSI results are suitable for subsequent data analysis and enable ROSI to be performed alone or as a preprocessing data reduction step. A full description of ROSI is presented along with results from both model and real hyperspectral data, and its performance is compared quantitatively to conventional class of data reduction methods.
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Leanna N. Ergin, Venkata N. K. Rao Bobba, and John F. Turner II "Unassisted reduction and segmentation of large hyperspectral image datasets", Proc. SPIE 10395, Optics and Photonics for Information Processing XI, 103950M (24 August 2017);

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