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Φ-Net: Deep Residual Learning for InSAR Parameters Estimation
2021
IEEE Transactions on Geoscience and Remote Sensing
Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named -Net, for the joint estimation of
doi:10.1109/tgrs.2020.3020427
dblp:journals/tgrs/SicaGRB21
fatcat:2gsgkskhdna3rnql6vgnkwdbma