This paper deals with the problem of constant false alarm rate detection of extended objects in high-resolution synthetic aperture radar (SAR) images. Starting from the receiver proposed by Guida and Longo, where some relevant properties of the location-scale distributions are exploited for ensuring constant false alarm against Weibull background, the authors consider the application of the algorithm to the case of spotlight SAR images collected by an airborne sensor developed by the Defense Evaluation and Research Agency laboratories. The algorithm has been reconceived for specific application to high-resolution SAR images where the reference window is order of magnitudes larger than in the usual radar case. As a first step, a careful analysis based on some real images has been made for testing the validity of the Weibull distribution hypothesis. Results are then presented for different values of the system parameters (censoring depth and size of the reference window) and demonstrate that the proposed detector is able to operate under changeable conditions of the underlying background (vegetation and asphalt mixed with grass) and of the object size and shape. A little care, however, should be taken to avoid overload situations that occur when the object dimensions become comparable with the size of the reference window. Finally, real-time applications may be foreseen, since the computational charge of the processor is generally low and increases weakly with the number of samples used for parameter estimation.

The paper deals with the detection of signals with unknown parameters in impulsive noise, modeled as a spherically symmetric random process. The proposed model subsumes several interesting families of noise amplitude distributions: generalized Cauchy, generalized Laplace, generalized Gaussian, contaminated normal. It also allows handling of the case of correlated noise by a whitening approach. The generalized maximum likelihood decision strategy is adopted, resulting in a canonical detector, which is independent of the amplitude distribution of the noise. A general method for performance evaluation is outlined, and a comprehensive performance analysis is carried out for the case of M-ary equal-energy orthogonal signals under several distributional assumptions for the noise. The performance is contrasted with that of the maximum likelihood receiver for completely known signals, so as to assess the loss due to the a-priori uncertainty as to the signal parameters.

Multispectral sensors using array of detectors are affected by striping, an artifact that appears as a series of horizontal bright or dark periodic lines in the remotely sensed images. Nonlinearities and memory effect of detectors are the main causes of the striping problem that is not effectively corrected in the onboard or postprocessing calibration phases. In order to clear striping from images, we consider a new procedure based on detector response equalization and apply it to moderate resolution imaging spectroradiometer data from Terra and Aqua satellites. After identification of the out-of-family detectors, a least squares equalization stage is considered for calibration by using the intrinsic data redundancy caused by the bow-tie effect, where multiple observations of the same field of view are available fr.om different detectors. The main advantage of this method, with respect to others such as the histogram equalization, is due to the independence of the measurements on the scene statistics, which, otherwise, will cause an overestimation or underestimation of the detectors' responses. The new procedure performance is validated using data received at the Mediterranean Agency for Remote Sensing and environmental control ground station facility in benevento-Italy and data downloaded from NASA LAADS Web site. The main results are presented, by showing the effectiveness of the method and the stability of the correction coefficients, at least on one-orbit periods.

When GNSS signals reflect off of the surfaces of lakes, rivers, wetlands, and other inland water bodies, the surfaces are often sufficiently smooth to produce coherent reflections. The observable produced from coherent reflections made by GNSS Reflectometry (GNSS-R) instruments exhibits particular features with respect to diffusely scattered signals by rough land and wind-driven oceans allowing detection of such smooth bodies. Several different GNSS-R coherence detection approaches have been reported in the literature and developed among the GNSS-R community over the last several years; however, the merits of each approach are difficult to compare because they are often applied to different scenarios and quantified in different ways, independently of each other. This paper provides a unified comparison of a wide variety of different GNSS-R coherence detection approaches, which is the most extensive published to date. The approaches are applied to a common data set from the NASA CYGNSS satellites that includes both the standard Level-1 DDM science product as well as raw baseband signal recordings. Additionally, simulated observables are generated with varying coherent and non-coherent reflection components to exercise algorithms over a wide range of SNRs and relative powers. Objective measures of accuracy are used to quantify the performance of each approach in the context of relative implementation complexity. Conclusions are presented on the pros/cons of the various methods as they relate to various applications such as real-time in-orbit coherence detection or post-processing on the ground.

A novel metric for detecting coherence in global navigation satellite system reflectometry (GNSS-R) signals is presented and evaluated. It applies the Von Neumann information entropy metric for density matrices, a powerful indicator of the degree of mixing between states, coherent and incoherent, of the scene under investigation. The metric is applied to a set of raw IF data acquired by the cyclone global navigation satellite system (CYGNSS) observatories over Lake Okeechobee FL, in order to test the sensitivity of the entropy to different land cover types, including wetlands and open water. Visual comparison of results with Sentinel-1 images provides a first step in the validation of the effectiveness of entropy in detecting the presence of water covered by emergent vegetation. In addition, the entropy-based metric could be implemented on future space-based GNSS-R receivers to adapt the incoherent integration times to the observed scene, thus achieving an improvement in along-track resolution.

The Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS) created the GRSS “Standards for Earth Observation Technical Committee” to advance the usability of remote sensing products by experts from academia, industry, and government through the creation and promotion of standards and best practices. In February 2019, a Project Authorization Request was approved by the IEEE Standards Association (IEEE-SA) with the title “Standard for Spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) Data and Metadata Content.” At present, 4 GNSS constellations cover the Earth with their navigation signals: The United States of America (USA) Global Positioning System GPS with 31 Medium Earth Orbit (MEO) operational satellites, the Russian GLObal'naya NAvigatsionnaya Sputnikovaya Sistema GLONASS with 24 MEO operational satellites, the European Galileo with 24 MEO operational satellites, and the Chinese BeiDou-3 with 3 Inclined GeoSynchronous Orbit (IGSO), 24 MEO, and 2 Geosynchronous Equatorial Orbit (GEO) operational satellites. Additionally, several regional navigation constellations increase the number of available signals for remote sensing purposes: the Japanese Quasi-Zenith Satellite System QZSS with 1 GSO and 3 Tundra-type orbit operational satellites, and the Indian Regional Navigation Satellite System IRNSS with 3 GEO and 4 IGSO operational satellites. On the other hand, there are different GNSS-R processing techniques, instruments and spaceborne missions, and a wide variety of retrieval algorithms have been used. The heterogeneous nature of these signals of opportunity as well as the numerous working methodologies justify the need of a standard to further advance in the development of GNSS-R towards an operational Earth Observation technique. In particular, the scope of this working group is to develop a standard for data and metadata content arising from past, present, and future spaceborne missions such as the United Kingdom (UK) TechDemoSat-1 TDS-1, and the National Aeronautics and Space Administration (NASA) CYclone Global Navigation Satellite System CYGNSS constellation coordinated by the University of Michigan (UM). In this article we describe the scene study, including fundamental aspects, scientific applications, and historical milestones. The spaceborne standard is under development and it will be published in IEEE-SA.