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Proceedings Paper

Region feature extraction and classification
Author(s): Deshen Xia; Hua Li
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Paper Abstract

Since the 1970s land remote sensing images have been widely used in environment protection, territory management, military, and so on. Moreover, the remote sensing images provide the whole body of information of some regions like mountains, rivers, towns, villages, oceans and other land objectives. How do we extract the significant parts we recovered from the remote sensing images? This paper demonstrates use of the single value feature extraction method, which uses the single value decomposition based on mesh samples to obtain the eigenvalues of the image matrixes. The SPOT satellite remote sensing images are used as models. The size of the meshes are defined by 100 X 100 pixels. Using this method, we experimented on four typical regions and acquired optimal vectors and succeeded in the recognition and classification of the regions. Also, this method has a high speed in computation. The above listed reasons proved that the single value decomposition method is efficient in the classification and the recognition of the typical regions of remote sensing images.

Paper Details

Date Published: 10 October 1994
PDF: 6 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188933
Show Author Affiliations
Deshen Xia, East China Institute of Technology (China)
Hua Li, East China Institute of Technology (China)


Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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