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Proceedings Paper

Agricultural field recognition by the method of a constrained optimization
Author(s): Fang Luo; Liu Lu; Nanno J. Mulder
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Paper Abstract

In this paper, a model based method to recognize agricultural fields is presented and demonstrated. At first, the task of the recognition is formulated as the problem of cost minimization. The approach is implemented through an hypothesis-correction-improvement process which usually starts with an initial hypothesis of object feature, compares it with the measured one, and then a cost can be calculated to control the improvement process. In this method, firstly, the cost is a function of the object shape parameters, and its value indicates the difference between the predicted and measured feature of an object. Secondly, to drive the cost to its minimum, the method used geometrical and topological properties of objects to constrain the optimization procedure. This prior knowledge helps the method reach the global minimum instead of a local one. The object recognition experiments performed on the high noise images (SAR) and the comparison results between different search strategies are given.

Paper Details

Date Published: 30 December 1994
PDF: 10 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196769
Show Author Affiliations
Fang Luo, International Institute for Aerospace Survey and Earth Sciences (Netherlands)
Liu Lu, International Institute for Aerospace Survey and Earth Sciences (Netherlands)
Nanno J. Mulder, International Institute for Aerospace Survey and Earth Sciences (Netherlands)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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