A neural network architecture for automatic extraction of oceanographic features in satellite remote sensing imagery

The authors discuss an approach for automatic feature detection and sensor fusion in remote sensing imagery using a combination of neural network architecture and Dempster-Shafer theory of evidence. Deterministic or idealized shapes are used to characterize surface signatures of oceanic and atmospherically fronts manifested in satellite remote sensing imagery. Raw satellite images are processed through a bank of radial basis function (RBF) neural networks trained on idealized shapes. The final classification results from the fusion of the outputs of the separate RBF. The fusion mechanism is based on Dempster-Shafer (DS) evidential reasoning theory. The approach is initially tested for detecting different features on a single sensor, and then is extended to classifying features observed in multiple sensors