Near Real-Time Enhancement of Fractional SAR Imagery Via Adaptive Maximum Entropy Neural Network Computing
Conference: EUSAR 2012 - 9th European Conference on Synthetic Aperture Radar
04/23/2012 - 04/26/2012 at Nuremberg, Germany
Proceedings: EUSAR 2012
Pages: 4Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Shkvarko, Yuriy V.; Santos, Stewart R.; Tuxpan, José (CINVESTAV del IPN, Mexico)
We address a neural network (NN) computing-based approach to the problem of near real-time enhancement of compressed fractional SAR imagery. The proposed approach employs the recently developed descriptive experiment design regularization (DEDR) framework for multimode image reconstruction/fusion aggregated with the variational analysis (VA) image enhancement paradigm implemented computationally via the new speeded-up adaptive maximum entropy neural network (MENN) processing technique. The developed DEDRVA- optimal MENN enhancement technique outperforms the recently proposed competing methods both in the achievable resolution enhancement and the convergence rates that is verified via reported computer simulations.