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Proceedings Paper

Model-based recognition and classification for surface texture of vegetation from an aerial sequence of images
Author(s): Haijun Chen; Zweitze Houkes
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Paper Abstract

In this paper, a model based recognition and classification method for surface texture of vegetation from aerial sequence of images is presented. The image sequences are assumed to be acquired by a video camera (RGB-CCD system) from an aeroplane, which moves linearly over the scene. The objects in the scenes being considered in this paper, are agricultural fields. The classes of agricultural fields to be distinguished are determined by the type of crop, e.g. potatoes, sugar beet, what, etc. In order to recognize and classify these fields from aerial sequence of images, a common approach is in the use of surface texture. Here the circular symmetric auto- regressive (CSAR) random model is used for texture analysis. By manipulating the estimated value against its real value, the characteristics of a texture image may be determined. A hypothesize-and verify algorithm is used for model recognition. Based on all kinds of models, classification for surface texture of vegetation from aerial sequences of images is realized.

Paper Details

Date Published: 30 December 1997
PDF: 10 pages
Proc. SPIE 3222, Earth Surface Remote Sensing, (30 December 1997); doi: 10.1117/12.298132
Show Author Affiliations
Haijun Chen, Univ. of Twente (Netherlands)
Zweitze Houkes, Univ. of Twente (Netherlands)

Published in SPIE Proceedings Vol. 3222:
Earth Surface Remote Sensing
Giovanna Cecchi; Edwin T. Engman; Eugenio Zilioli, Editor(s)

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