In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques. Index Terms-SAR, speckle, multiplicative noise, convolutional neural networks.
In the context of SAR image despeckling, the use of convolutional neural networks (CNNs) has already been studied successfully REF .
SAR image despeckling through convolutional neural networks
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Computer Science