Model-Based Pseudo-Quad-Pol Reconstruction from Compact Polarimetry and Its Application to Oil-Spill Observation

Compact polarimetry is an effective imaging mode for wide area observation, especially for the open ocean. In this study, we propose a new method for pseudo-quad-polarization reconstruction from compact polarimetry based on the three-component decomposition. By using the decomposed powers, the reconstruction model is established as a power-weighted model. Further, the phase of the copolarized correlation is taken into consideration. The phase of double-bounce scattering is closer to π than to 0, while the phase of surface scattering is closer to 0 than to π. By considering the negative (double-bounce reflection) and positive (surface reflection) copolarized correlation, the reconstruction model for full polarimetry has a good consistency with the real polarimetric SAR data. L-band ALOS/PALSAR-1 fully polarimetric data acquired on August 27, 2006, over an oil-spill area are used for demonstration. Reconstruction performance is evaluated with a set of typical polarimetric oil-spill indicators. Quantitative comparison is given. Results show that the proposed model-based method is of great potential for oil-spill observation

A ship detector applying Principal Component Analysis to the polarimetric Notch Filter

Ship detection using polarimetric synthetic aperture radar (PolSAR) data has attracted a lot of attention in recent years. Polarimetry can provide information regarding the scattering mechanisms of targets, which helps discriminate between ships and sea clutter. This enhancement is particularly valuable when we aim at detecting smaller vessels in rough sea states. This work exploits a ship detector called the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF), and it is aimed at improving its performance especially when less polarimetric images are available (e.g., dual-polarimetric data). The idea is to design a new polarimetric feature vector containing more features that are renowned to allow separation between ships and sea clutter. Then, a Principal Component Analysis (PCA) is further used to reduce the dimensionality of the new feature space. Experiments on four real Sentinel-1 datasets are carried out to demonstrate the validity of the proposed method and compare it against other ship detectors. Analyses of the experimental results show that the proposed algorithm can not only reduce the false alarms significantly, but also enhance the target-to-clutter ratio (TCR) so that it can more effectively detect weaker ships

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Structural, Magnetic, Dielectric, Electrical, Optical and Thermal Properties of Nanocrystalline Materials: Synthesis, Characterization and Application

This book is a collection of the research articles and review article, published in special issue "Structural, Magnetic, Dielectric, Electrical, Optical and Thermal Properties of Nanocrystalline Materials: Synthesis, Characterization and Application"