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Proceedings Paper

Simultaneous hierarchical segmentation and vectorization of satellite images through combined non-uniform data sampling and anisotropic triangulation
Author(s): Jacopo Grazzini; Scott Dillard; Lakshman Prasad
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Paper Abstract

The automatic detection, recognition, and segmentation of object classes in remote sensed images is of crucial importance for scene interpretation and understanding. However, it is a difficult task because of the high variability of satellite data. Indeed, the observed scenes usually exhibit a high degree of complexity, where complexity refers to the large variety of pictorial representations of objects with the same semantic meaning and also to the extensive amount of available details. Therefore, there is still a strong demand for robust techniques for automatic information extraction and interpretation of satellite images. In parallel, there is a growing interest in techniques that can extract vector features directly from such imagery. In this paper, we investigate the problem of automatic hierarchical segmentation and vectorization of multispectral satellite images. We propose a new algorithm composed of the following steps: (i) a non-uniform sampling scheme extracting most salient pixels in the image, (ii) an anisotropic triangulation constrained by the sampled pixels taking into account both strength and directionality of local structures present in the image, (iii) a polygonal grouping scheme merging, through techniques based on perceptual information, the obtained segments to a smaller quantity of superior vectorial objects. Besides its computational efficiency, this approach provides a meaningful polygonal representation for subsequent image analysis and/or interpretation.

Paper Details

Date Published: 23 October 2010
PDF: 13 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300F (23 October 2010); doi: 10.1117/12.865047
Show Author Affiliations
Jacopo Grazzini, Los Alamos National Lab. (United States)
Scott Dillard, Pacific Northwest National Lab. (United States)
Lakshman Prasad, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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