Real-time analysis of ships in radar images with neural networks
This paper shows the effectiveness of using neural paradigms to filter, enhance and restore radar images of ships sailing in low visibility conditions. The task, to be accomplished in real time, is inserted in a processing chain which provides the mobile trajectory. Adaptive vector quantization techniques (supervised and unsupervised) and back-propagation neural algorithms are considered. The final optimized architecture contains a small set of processing modules comprising, at most, two pipelined neural convolvers (an enhancing filter and a classifier). Each neural-convolver mask is characterized by a symmetrical structure whose optimal coefficients have been determined via learning.
neural networks feature extraction optimized learning pattern classification recognition symmetrical convolvers