Advanced Synthetic Aperture Radar (SAR) systems acquire a large volume of data, which necessitates the development of efficient data compression methods, beyond the current conventional techniques. Sentinel-1, as one the most popular SAR missions, provides global freely accessible data. However, the available raw data (i.e., Level-0 products) are quantized before being transferred, thus the statistics are different, hindering the validation of new algorithms mainly based on machine/deep learning paradigms. To enable elaboration of further SAR raw data compression, in this study, we propose a procedure to add random quantization noise to the decoded Sentinel-1 SAR raw data in order to obtain adapted uniformly quantized raw data that resemble the statistics of the uncompressed SAR raw data onboard the satellites. This method opens further opportunities to create large benchmarks for SAR raw data for data compression and other applications. The performance of data compression techniques (Block Adaptive Quantization (BAQ) and a complex-valued autoencoder-based data compression scheme) is evaluated on the adapted uniformly quantized raw data, and the effectiveness of the defined procedure is demonstrated.

The application of deep learning methods in various fields is rapidly increasing. The development of complex-valued (CV) networks that can process CV data has provided many opportunities for utilizing the immense capabilities of deep networks for CV data, including Synthetic Aperture Radar (SAR). However, the physical model and basic properties of the original SAR data must be preserved in the CV architecture. Without these properties, the physical parameters cannot be accurately retrieved from the SAR data. This study evaluates the competency of CV deep architectures to preserve the properties of the original SAR data and how it affects the retrieval of physical parameters. Ocean Surface Current (OSC) is an important parameter for ocean circulation and plays a vital role globally. In this work, the correlation Doppler estimation (CDE) method is used to estimate the OSC from SAR data before and after reconstruction with the CV autoencoder. The obtained OSCs are compared, and we demonstrate the ability of the CV deep architectures to learn the data model and preserve the original Doppler centroid (fDC) information in the SAR data. This research paves the way for the development of CV deep architectures for physical parameter retrieval and prediction from CV SAR data in future studies.

Recent advances in Synthetic Aperture Radar (SAR) sensors have enabled the acquisition of very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. As a result of the significant increase of SAR data size, an effective compression of the acquired data is of paramount importance. However, conventional data compression methods demonstrate limited effectiveness when applied to SAR data. In order to tackle this problem, in this study, a Complex-Valued (CV) end-to-end deep learning-based architecture based on convolutional autoencoders is proposed to compress Single Look Complex (SLC) SAR data. By relying on dual polarization SAR data, one of the polarization channels of the data is used as the side information to assist the reconstruction of the compressed channel with lower data loss. The obtained results demonstrate the remarkable potential and capability of CV deep learning-based methods for SAR data compression.

Deep learning models have achieved remarkable success in many different fields and attracted many interests. Several researchers attempted to apply deep learning models to Synthetic Aperture Radar (SAR) data processing, but it did not have the same breakthrough as the other fields, including optical remote sensing. SAR data are in complex domain by nature and processing them with Real-Valued (RV) networks neglects the phase component which conveys important and distinctive information. A Complex-Valued (CV) end-to-end deep network is developed in this study for the reconstruction and classification of CV-SAR data. Azimuth subaperture decomposition is utilized to incorporate physics-aware attributes of the CV-SAR into the deep model. Moreover, the correlation coefficient amplitude (Coherence) of the CV-SAR images depends on the SAR system characteristics and physical properties of the target. This coherency should be considered and preserved in the processing chain of the CV-SAR data. The coherency preservation of the CV deep networks for CV-SAR images, which is mostly neglected in the literature, is evaluated in this study. Furthermore, a large-scale CV-SAR annotated dataset for the evaluation of the CV deep networks is lacking. A semantically annotated CV-SAR dataset from Sentinel-1 Single Look Complex StripMap mode data (S1SLC_CVDL dataset) is developed and introduced in this study. The experimental analysis demonstrated the better performance of the developed CV deep network for CV-SAR data classification and reconstruction in comparison to the equivalent RV model and more complicated RV architectures, as well as its coherency preservation and physics-aware capability.

Despite the state-of-the-art performance of the deep learning methods for Synthetic Aperture Radar (SAR) data classification, the Real-Valued (RV) networks neglect the phase component of the Complex-Valued (CV) SAR data and lose a lot of useful information. CV deep architectures have been developed in the recent years to exploit the amplitude and phase components of the CV data, in different fields. However, the superiority of CV models over RV models are proved to be different for each application, and more investigation into the advantages and disadvantages of implementing CV models for SAR data classification is necessary. In this study, the performance of the CV Convolutional Neural Network (CV-CNN) for Polarimetric SAR (PolSAR) data classification is compared with its RV equivalent network, in different contexts.

Land cover maps are among the most important products of Remote Sensing (RS) imagery. Despite remarkable advancements in land cover classification techniques, abundant detailed information in the very high-resolution RS images necessitates further improvements to harness the data and discover detailed semantic information. Moreover, scarcity of the labelled data and its quality is a major limitation in RS land cover mapping. In the present study, Latent Dirichlet Allocation is employed for semantic discovery in RS images and a novel kernel-based Bag of Visual Words model is proposed for land cover mapping.