Patch-based image segmentation of satellite imagery using minimum spanning tree construction [electronic resource].

Published: Washington, D.C. : United States. Dept. of Energy, 2010.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Additional Creators: Los Alamos National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information

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Summary

We present a method for hierarchical image segmentation and feature extraction. This method builds upon the combination of the detection of image spectral discontinuities using Canny edge detection and the image Laplacian, followed by the construction of a hierarchy of segmented images of successively reduced levels of details. These images are represented as sets of polygonized pixel patches (polygons) attributed with spectral and structural characteristics. This hierarchy forms the basis for object-oriented image analysis. To build fine level-of-detail representation of the original image, seed partitions (polygons) are built upon a triangular mesh composed of irregular sized triangles, whose spatial arrangement is adapted to the image content. This is achieved by building the triangular mesh on the top of the detected spectral discontinuities that form a network of constraints for the Delaunay triangulation. A polygonized image is represented as a spatial network in the form of a graph with vertices which correspond to the polygonal partitions and graph edges reflecting pairwise partitions relations. Image graph partitioning is based on the iterative graph oontraction using Boruvka's Minimum Spanning Tree algorithm. An important characteristic of the approach is that the agglomeration of partitions is constrained by the detected spectral discontinuities; thus the shapes of agglomerated partitions are more likely to correspond to the outlines of real-world objects.

Report Numbers

E 1.99:la-ur-10-02273
E 1.99: la-ur-10-2273
la-ur-10-2273
la-ur-10-02273

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Note

Published through SciTech Connect.
01/01/2010.
"la-ur-10-02273"
" la-ur-10-2273"
GEOBIA-2010: Geographic Oblect-Based Image Analysis ; June 29, 2010 ; Gihent, Belgium.
Skurikhin, Alexei N.

Funding Information

AC52-06NA25396

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