Region feature extraction and classification

Deshen Xia; Hua Li

doi:10.1117/12.188933

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10 October 1994 Region feature extraction and classification

Deshen Xia, Hua Li

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Proceedings Volume 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision; (1994) https://doi.org/10.1117/12.188933
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

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.

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Deshen Xia and Hua Li "Region feature extraction and classification", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); https://doi.org/10.1117/12.188933

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