An application of data fusion to landcover classification of remote sensed imagery: a neural network approach

This paper focuses on the possibilities offered by neural networks applied to multisensor image data processing. The great number of existing and planned instruments for Earth observation (satellites, sensors) highlights the need of specific techniques for processing, and, in particular, for merging, the large amount of data that will be available in future years. Moreover emphasis is given to the importance of fusing data acquired by sensors operating in different regions of the electromagnetic spectrum. Neural networks (NNs) are employed to perform fusion of TM data with SAR data in order to obtain a landcover classification of an agricultural area in the surroundings of Florence (Italy). Two different architectures of NN are presented and employed, the counterpropagation network and the Kohonen map; the results obtained in both cases are reported and discussed.<>