Diffusion Geometry Based Nonlinear Methods for Hyperspectral Change Detection
Final rept. 15 Jul 2001-14 Apr 2010
PLAIN SIGHT SYSTEMS INC NEW HAVEN CT
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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.
- Optical Detection and Detectors