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Image-driven data mining methods are described for image content segmentation, classification, and attribution, where each pixel location of an image-under-analysis is the center point of a pixel-block query that returns an estimated class label. Feature attribute estimates may also be mined when sufficient attribute strata exist in the data warehouse. Novel methods are presented for pixel-block mining, pattern similarity scoring, class label assignments, and attribute mining. These methods are based on a direct sum tree structure called a σ-tree that is utilized with near-neighbor similarity scoring. The σ-tree structure provides a solution to the challenge of high computation/memory costs of pixel-block similarity searching. The σ-trees are integrated into warehouse subsystems that provide referential capability into feature attribute data, resulting in a foundation for data mining called Source Optimized, Labeled, DIgital Expanded Representations (SOLDIER). The variable depth "bit-plane" data representations produced by σ-tree path selections provide an approach to image content segmentation, and provide a structure for formulation of Bayesian classification with data-adaptive Parzen classifiers with variably sized windows. Preliminary methods and results for postprocessing of mined feature-thematic layers for higher level scene understanding are also presented. Sample results are shown with synthetic aperture radar images and with high-resolution pan-sharpened satellite images of the Payagala, Sri Lanka area before the site was devastated by the 2004 Asian Tsunami. [ABSTRACT FROM AUTHOR]

Detection, classification, and attribution of high- resolution satellite image features in nearshore areas in the aftermath of Hurricane Katrina in Gulfport, MS, are investigated for damage assessments and emergency response planning. A system-level approach based on image-driven data mining with σ-tree structures is demonstrated and evaluated. Results show a capability to detect hurricane debris fields and storm-impacted nearshore features (such as wind-damaged buildings, sand de- posits, standing water, etc.) and an ability to detect and classify nonimpacted features (such as buildings, vegetation, roadways, railways, etc.). The σ-tree-based image information mining capability is demonstrated to be useful in disaster response planning by detecting blocked access routes and autonomously discovering candidate rescue/recovery staging areas. [ABSTRACT FROM AUTHOR]