Parameter Prediction Method of SAR Target Simulation Based on Convolutional Neural Networks

Konferenz: EUSAR 2018 - 12th European Conference on Synthetic Aperture Radar
04.06.2018 - 07.06.2018 in Aachen, Germany

Tagungsband: EUSAR 2018

Seiten: 5Sprache: EnglischTyp: PDF

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Autoren:
Niu, Shengren (University of Chinese Academy of Sciences & Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, P.R. China)
Qiu, Xiaolan; Lei, Bin (Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences)
Peng, Lingxiao (Suzhou Institute, Institute of Electronics, Chinese Academy of Sciences, China)

Inhalt:
SAR image simulation plays a useful role in SAR target interpretation and recognition. The current SAR target simulation methods require high precision of models and simulation parameters, and are only forward processes which lack the feedback adjustment of real image. In this paper, a method of predicting simulation parameters from real images is proposed, which is based on convolutional neural networks(CNN). The architecture and the loss function of the CNN are modified to obtain better performance of the parameter inversion. From the simulation results, the predicted parameters improve the similarity between the simulation images and the real images.