Neural image fusion of remotely sensed electro-optical and synthetic aperture radar data for forest classification

Although the processing of electro-optical imagery from Earth observation satellites has been effectively used for classification of many types of land cover, forest classification has been generally limited to broad categories such as deciduous or coniferous. Recent studies suggest that the combination of imagery from satellites with different spectral, spatial, and temporal information may improve classification performance. This paper discusses the results of new fusion research aimed at extracting additional information from the combination of multi-sensor imagery to improve forest classification performance. For this investigation multi-season LANDSAT and RADARSAT imagery was combined using a new biologically-based opponent-color image fusion and data mining technique, in conjunction with visual texture enhancement, and the Fuzzy ARTMAP neural classifier [1]. This approach is shown to quickly learn individual forest classes from a small number of training examples and enable added-value assessment of different sensor modalities.