Accession Number : ADA524546


Title :   Diffusion Geometry Based Nonlinear Methods for Hyperspectral Change Detection


Descriptive Note : Final rept. 15 Jul 2001-14 Apr 2010


Corporate Author : PLAIN SIGHT SYSTEMS INC NEW HAVEN CT


Personal Author(s) : Coifman, Ronald ; Coppi, Andreas ; Hirn, Matthew ; Warner, Frederick


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a524546.pdf


Report Date : 12 May 2010


Pagination or Media Count : 138


Abstract : Throughout this Phase I project, we have integrated a suite of nonlinear signal processing algorithms derived from diffusion geometry into an existing proprietary Hyperspectral processing toolbox. These methods enable the organization and comparison of spatio-spectral features of hyperspectral images acquired under different conditions, for target detection, change and anomaly assessment. The main ingredients in our approach involve a high level geometrization of spatio spectral signatures. We developed an approach to simultaneously segment a scene in terms of similarities of spatio spectral signatures at different inference as well as a partition of the feature space of spectra and morphology into groups of features related to the various locations on the scene. We refer to this approach in which we interrogate and organize both the pixels and their responses as the questionnaire organization paradigm. This spectral segmentation methodology is critical for change detection as it enables to isolate changes by comparing their relation to their spatio-spectral folders. The folder identity provides invariant features for change detection.


Descriptors :   *CHANGE DETECTION , *SIGNAL PROCESSING , *HYPERSPECTRAL IMAGERY , SEGMENTED , NONLINEAR ANALYSIS , ALGORITHMS , TARGET DETECTION


Subject Categories : Cybernetics
      Optical Detection and Detectors


Distribution Statement : APPROVED FOR PUBLIC RELEASE