Recursive unsupervised neural network approach to extract concepts from remote sensing images
Jean-Pierre Novak, Jerzy J. Korczak · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
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.