Spacial classification and multi-spectral fusion with neural networks

Abstract

Neural networks classified a thematic mapper LandSat 4 multi-spectral image of the area surrounding Murfree=boroS Tennessee. Back propagation neural network= were trained to identify different land types. Six area% were partially classified with individual networks for each band. The results were combined/fused with a network to categorize all six areas. In another analysis~ contiguous points of Maximum– likelihood classifications (MLC) were reclassified by a neural network. Amoung other things~ this network learned to distinguish between buildings and rocks that were classified the same by the MLC. Clearly Permissiontocopy withoutfeeallorpart ofthismatenalis granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and tie title of dre publication and its date appear, and notice is given that copying is by permissionoftheAssoeiation forComputingMachinery. Tocopy otherwise, or to republish requires a fee and/or specific permission. neural networks can be used for multi-spectral classification. The combination of MLC and neural techniques is productive. Real time multi–spectral processing may be possible with neural network hardware. INTRCHXJCTION Multi-spectral remotely sensed data i% used to classify areas of the earth. Urban areas can be identified~ crops quantified, forests evaluated and oceans studied. Multi– spectral classification techniques are well proven and commercially viable (Richa%an~