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High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

Authors :
Y. Yang
H. T. Li
Y. S. Han
H. Y. Gu
Source :
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-7/W4, Pp 197-201 (2015)
Publication Year :
2015
Publisher :
Copernicus Publications, 2015.

Abstract

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

Details

Language :
English
ISSN :
16821750 and 21949034
Volume :
XL-7/W4
Database :
Directory of Open Access Journals
Journal :
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.65a5f89560e4278985a74c90e54ac01
Document Type :
article
Full Text :
https://doi.org/10.5194/isprsarchives-XL-7-W4-197-2015