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Proceedings Paper

Feature extraction, anomaly, and change detection on WorldView-2 imagery by hierarchical image segmentation: a study
Author(s): Lakshman Prasad; James Theiler; Matthew Fair; Sriram Swaminarayan
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Paper Abstract

We study spatio-spectral feature extraction and image-adaptive anomaly and change detection on 8-band WorldView 2 imagery using a hierarchical polygonal image segmentation scheme. Features are represented as polygons with spectral and structural attributes, along with neighborhood structure and containment hierarchy for contextual feature identification. Further, the hierarchical segmentation provides multiple, coarse-scale, sub-backgrounds representing relatively uniform regions, which localize and simplify the spectral distribution of an image. This paves the way for facilitating anomaly and change detection when restricted to the contexts of these backgrounds. For example, forestry, urban areas, and agricultural land have very different spatio-spectral characteristics and their joint contribution to the image statistics can result in a complex distribution against which detecting anomalies could in general be a challenging problem. Our segmentation scheme provides sub-regions in the later stages of the hierarchy that correspond to homogeneous areas of an image while at the same time allowing inclusion of distinctive small features embedded in these regions. The exclusion of other image areas by focusing on these sub-backgrounds helps discover these outliers more easily with simpler methods of discrimination. By selecting appropriate bands in WorldView2 imagery, the above approach can be used to achieve fine spatio-spectral control in searching and characterizing features, anomalies, and changes of interest. The anomalies and changes are also polygons, which have spectral and structural attributes associated with them, allowing further characterization in the larger context of the image. The segmentation and feature detections can be used as multiple layers in a Geospatial Information System (GIS) for annotating imagery.

Paper Details

Date Published: 24 May 2012
PDF: 11 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901U (24 May 2012); doi: 10.1117/12.919295
Show Author Affiliations
Lakshman Prasad, Los Alamos National Lab. (United States)
James Theiler, Los Alamos National Lab. (United States)
Matthew Fair, Los Alamos National Lab. (United States)
Sriram Swaminarayan, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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