Classification of remotely-sensed image data using artificial neural networks

1 November 1991

journal article
research article
Published by Taylor & Francis in International Journal of Remote Sensing

Vol. 12 (11) , 2433-2438
https://doi.org/10.1080/01431169108955275

Abstract

Artificial neural networks have been used recently for speech and character recognition. Their application for the classification of remotely-sensed images is reported in this Letter. Remotely sensed image data are usually large in size and spectral overlaps among classes of ground objects are common. This results in low convergence performance of the Back-Propagation Algorithm in a neural network classifier. A Blocked Back-Propagation (BB-P) algorithm was proposed arid described in this Letter. It improved convergence performance and classification accuracy.

Keywords

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