Remotely sensed image fusion with dynamic neural networks

Presents the dynamic Hopfield-type multistate maximum entropy neural network (MENN) for image restoration with data-controlled system fusion. The optimal fusion was accomplished by processing the data provided by several imaging systems incorporating measurements, system calibration and image model information. Applying the developed new aggregation method we performed an optimal adjustment of the parameters of the MENN algorithm by simultaneously controlling the data acquisition balance and resolution-to-noise balance in the fused restored image. Due to this applied system aggregation method the developed MENN exhibited substantially improved resolution performance if compared with those with existing neural-network-based and traditional regularized inversion techniques, which do not accomplish the system fusion tasks.