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Proceedings Paper

Comparison between object- and pixel-level approaches for change detection in multispectral images by using neural networks
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Paper Abstract

We propose in this paper the investigation of the change detection approaches based on the pixel level and the object level. The pixel level approach is based on the simultaneous analysis of multitemporal data, while the object level approach uses a comparative analysis of independently produced classifications of data. Thereby, the comparison is established by using the multilayer neural network classifier. Usually, the backpropagation algorithm is used as a training rule. In this paper, we investigate the use of the Kalman filtering (KF) as the training algorithm for detecting changes in remotely sensed imagery. By using SPOT images and evaluation criteria, the detailed comparison indicates that the KF algorithm is preferable compared to the BP algorithm in terms of convergence rate, stability and change detection accuracy.

Paper Details

Date Published: 5 February 2004
PDF: 9 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.509934
Show Author Affiliations
Hassiba Nemmour, Univ. des Sciences et de la Technologie Houari Boumedienne (Algeria)
Youcef Chibani, Univ. des Sciences et de la Technologie Houari Boumedienne (Algeria)


Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

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