Modeling and Processing of Radar Signals for Earth Observation

The rapidly growing number of real aperture and synthetic aperture radars (SARs) devoted to Earth observation provides today a very broad coverage across space, time, and the electromagnetic spectrum. The all-weather, day, and night capabilities of these radar sensors permit acquisition of information under conditions that are not possible with EO sensors. Radars can monitor iceberg position, movement, and age to improve safety at sea. They can provide useful information on oceans and their currents. Radars can also explore vast areas of the Earth, providing an inventory of potential mineral resources, new transportation routes, freshwater supplies, sites for agriculture, and so on. This editorial serves as an introduction to some of the many varied applications of radar that are being researched today. Synthetic aperture radar (SAR), whether from space or airborne platforms, continues to attract much attention, including the analysis of SAR imagery as a way to classify targets such as ships and urban buildings, using features obtained from polarimetric and interferometric SAR images. Interferometric techniques can also be applied to analyze the movement of targets in an SAR image. Polarimetric data can also be successfully used for classification purposes in inverse synthetic aperture radar (ISAR) images and there are applications for passive SAR bistatic systems. Other topics covered include the problem of the correct reconstruction of an image; robust techniques for high-resolution reconstructive imaging, applied to enhanced remote sensing (RS); a new descriptive experiment design regularization (DEDR) approach to treat the uncertain radar image enhancement/reconstruction problems; remote sensing using the application of satellite imagery for damage assessment; methods for fusing remotely sensed radar imagery with geographic databases for rapid assessment of earthquake damage; the use of real aperture radar in remote sensing of the environment; empirical modeling of sea clutter to provide the basis for inference of local sea conditions from remote low-grazing-angle radar returns; the characterization of sea clutter as a nonlinear dynamic process, based on the stochastic dynamics of radar sea scattering.