Neural network for change detection of remotely sensed imagery

C. F. Chen; Kun Shan Chen; J. S. Chang

doi:10.1117/12.226837

17 November 1995 Neural network for change detection of remotely sensed imagery

C. F. Chen, Kun Shan Chen, J. S. Chang

Proceedings Volume 2579, Image and Signal Processing for Remote Sensing II; (1995) https://doi.org/10.1117/12.226837
Event: Satellite Remote Sensing II, 1995, Paris, France

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.

Citation Download Citation

C. F. Chen, Kun Shan Chen, and J. S. Chang "Neural network for change detection of remotely sensed imagery", Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); https://doi.org/10.1117/12.226837

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