Radar image segmentation using self-adapting recurrent networks.
This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.
Ziemke
T
T
Department of Computer Science, University of Skövde, Sweden. tom@ida.his.se
D004301:Doppler Effect; D004784:Environmental Monitoring / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D007858:Learning; D016571:Neural Networks, Computer; D011825:Radar; D018511:Systems Integration