FULLY POLARIMETRIC SAR IMAGE DESPECKLING USING DEEP NEURAL NETWROK

PolSAR (Fully Polarimetric Synthetic Aperture Radar) imagery is used in Earth observation and remote sensing for various applications such as land use and land cover classification, change detection, etc. The occurrence of speckle in PolSAR images, however, degrades the performance of all image processing techniques and therefore prevents their full use for various applications. Several speckle reduction methods have been developed in the literature over the last forty years, highlighting the importance of this issue. Despite this extensive knowledge, speckle removal is yet an open problem that is far from being fully solved. Recently, Deep Learning (DL) has achieved great success in speckle reduction of SAR images. The data-driven nature of this technique provides improved flexibility and the ability to capture a variety of features from PolSAR images, thereby enhancing the performance of the speckle reduction process. In this paper, a new despeckling technique is proposed in the context of deep convolutional neural networks for de-noising the polarimetric covariance or coherence matrix. The method controls the training process with respect to several characteristic features of PolSAR images defined by the combination of three different cost functions. In particular, the goal is to balance the different features, including spatial details and speckle statistical properties, in the denoising process. The proposed method is experimentally validated with real airborne datasets and compared with existing despeckling approaches.