Recursive unsupervised neural network approach to extract concepts from remote sensing images

Jean-Pierre Novak; Jerzy J. Korczak

doi:10.1117/12.331875

4 December 1998 Recursive unsupervised neural network approach to extract concepts from remote sensing images

Jean-Pierre Novak, Jerzy J. Korczak

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331875
Event: Remote Sensing, 1998, Barcelona, Spain

Abstract

This paper describes a novel recursive and unsupervised learning method for extracting information from remote sensing images. Usually, the amount of data on these images is large, and the number of mixed pixels is important. Therefore, an unsupervised learning or clustering can be useful in the analysis of these data. An unsupervised neural network algorithm is used for initial segmentation of the spectral data space of remote sensing images. To discover concepts, a recursive region aggregation method is proposed. This method has been tested and validated with several remote sensing images. An urban zone image is used to illustrate this learning method which provides a way for fast and automatic segmentation of remote sensing images. In order to improve the efficiency of concept extraction some spatial information is incorporated into the aggregation procedure.

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Jean-Pierre Novak and Jerzy J. Korczak "Recursive unsupervised neural network approach to extract concepts from remote sensing images", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331875

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KEYWORDS

Image segmentation

Remote sensing

Machine learning

Neurons

Neural networks

Brain mapping

Image classification

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