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Proceedings Paper

Neural network for change detection of remotely sensed imagery
Author(s): C. F. Chen; Kun Shan Chen; J. S. Chang
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Paper Abstract

The use of a neural network for determining the change of landcover/land-use with remotely sensed data is proposed. In this study, a single image contains both spectral and temporal information is created from a multidate satellite imagery. The proposed change detection method can be divided into two main steps: training data selection and change detection. At the training step, the training set, basically consists of the classes of no-change and possible change data, is obtained from the composited image. Then the training data is used to input the neural network and obtain the network's weights. At the change detection step, the network's weights is employed to detect the change and no-change classes in the combined image. The proposed method is tested using a multidate SPOT imageries and a satisfied change pattern detection is obtained.

Paper Details

Date Published: 17 November 1995
PDF: 6 pages
Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226837
Show Author Affiliations
C. F. Chen, National Central Univ. (Taiwan)
Kun Shan Chen, National Central Univ. (Taiwan)
J. S. Chang, National Central Univ. (Taiwan)


Published in SPIE Proceedings Vol. 2579:
Image and Signal Processing for Remote Sensing II
Jacky Desachy, Editor(s)

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