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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 8, NO. 4, JULY 2011 745 Region-Based Spatial Preprocessing for End member Extraction and Spectral Unfixing Gabriel Mart n and Antonio Plaza, Senior

This approach is typically applied to image segments of spectral bands of different lengths. This paper proposes to unify spectral processing in end member extraction and unifying unadulterated end member extraction into a single-step processing stage. We propose to extend the spectral pipeline on end member acquisition and processing to produce a single spectral extraction step for a segment of the signal by applying spatial preprocessing to each and every pixel. During the segmentation stage, in addition to pixel identification, we need to estimate spectral density for any desired sub-bands and apply a spectral mask, thus obtaining the total spectral density. Our step in unifying unadulterated spectral extraction in end member processing allows us to apply the spectral extraction step only on segmented spectral bands of known length. The proposed step does not employ spectral masks, thus ensuring that end member spectral signals remain unaltered and that unadulterated end member signals are processed identically by the segmentation steps. The work is limited in that we have no reliable definition of end members for image content; this is remedied when the spectral analysis is run on a global image. The presented approach can be applied to an image of any dimensionality and any signal segmentation stage. It also implies a significantly simplified set of spectral extraction algorithms. We also note that a more realistic spectral analysis scheme needs some form of spatial segmentation to identify an end member. It is also notable that we derive the effective spectral density as the product of pixel intensity and mean pixel intensity, instead of as the product of pixel intensity, pixel bin intensity and mean pixel bin intensity. A more detailed analysis of our work is presented in S. Mart n and A. Plaza, “Sparse signal segmentation using spatial filtering and global identity,” Proceedings of the IEEE 2011 International Conference on Pattern Recognition. (arXiv:1212.

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