Detecting Dim Small Targets in Image Data Using Morphological Neural Networks of Background Adaptive Prediction

An effective morphological neural network of background clutter prediction for detecting dim small targets in image data was proposed.The target of interest was assumed to have a very small spatial spread,and was obscured by heavy background clutter.The clutter was predicted exactly by morphological neural networks and subtracted from the input signal,leaving components of the target signal in the residual noise.The traditional 3-layer feed forward BP network modal of morphological opening and closing operation was modified by extending the input layer data.For tracking complex background including different sub-structures,the raw image was partitioned to some sub-blocks,in which the training samples were chosen for optimizing the weights of structuring element in the corresponding block.Computer simulations of real image data show better performance compared with other traditional methods.