Tropical cyclones wind retrievals from satellite observations are typically associated with long revisit intervals, that limits the systems operational use especially in case of rapid intensification rates of the cyclones. Here, we use two observational satellite datasets in order to explore the spatiotemporal range of wind speed stability for possible fusion of data from different sensors. On one hand the CYGNSS platforms ensure relatively short revisit times, while on the other hand imaging radars, such as the Sentinel-1, provide high resolution images at longer revisit times. Results provide some preliminary valuable information on wind data variability in the cyclone moderate wind zone.

Several companies are developing massive satellite constellations aimed to deliver global broadband internet and provide in the near future benefits and opportunities. These opportunity signals also provide additional capabilities if exploited for remotes sensing purposes. The present study shows first results of an Open Space Innovation idea supported by the European Space Agency (ESA), where the concept of microwave reflectometry using signals of opportunity from the OneWeb satellites constellation will be used for wind speed measurements over the sea surface. This big constellation of satellites operate in the Ku band, which is well suited for detecting sea surface geophysical parameters as well as soil soil moisture. This work focuses on the simulation of the normalized bistatic radar cross section scattered from the sea surface using a full polarimetric approach in first-order small slope approximation.

Phase altimetry has been recently introduced for high precision surface height measurements in satellite global navigation satellite system reflectometry. In the presence of coherent scattering the effectiveness of the technique has been demonstrated for sea surface at low grazing angle as well as for rivers and lakes. This study develops the concept of differential phase altimetry for land surfaces, provided that the surface roughness is small and the surface undulations are gentle. Differential phase altimetry is more tolerant with respect to atmospheric and systematic errors but its applicability is difficult in the presence of surface slope variations that may generate phase cycle slips that require complex post processing to unwrap the retrieved phase. To mitigate cycle slips an overlapping correlation process is introduced to generate phase values with high spatial resolution. A first evaluation of performance is presented using the CYclone Global Navigation Satellite System raw intermediate frequency acquisitions over land surface with gentle undulations.

Earth’s inland water monitoring is probably the main promising application of Global Navigation Satellite Systems Reflectometry (GNSS-R) techniques. The ultimate spatial resolution under the coherent scattering regime deserves further investigation. The new Cyclone Global Navigation Satellite System (CYGNSS) raw Intermediate Frequency (IF) data product with a temporal resolution down to 2 ms could help to further understand this. In the framework of climate change the "water" is the "new gold". In-space water monitoring could help final users to make decisions with impact in several topics including geopolitics. The use of GNSS-R techniques by future constellations of SmallSats could overcome several limitations of more classical remote sensing techniques. In this work, a novel real-time inland water detector by a future GNSS-R receiver is presented. This detector, the so-called fast entropy Efast, shows the capability to detect small water bodies under thick biomass ~ 450 ton/ha in the Congo basin.

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.

Phase altimetry has been recently introduced for high precision measurements in satellite bistatic global navigation satel-lite system refiectometry in the presence of coherent scattering. This work introduces differential altimetry concept via a simulation study. The phase difference between two contigu-ous specular points paths is exploited to reduce atmospheric effects and systematic errors occurring over closely spaced paths. The work also introduces an oversampling technique to mitigate unwrapping errors.

The main contributions to signal decorrelation in Global Navigation Satellite Systems Reflectometry (GNSS-R) over the ocean are known to be the platforms motion and the sea surface roughness. These two actions play at different timescales: of the order of few milliseconds, the former and hundreds of milliseconds, the latter. Currently, bistatic scattering models as well as simulators do not provide a rationale for sea surface correlation, whose nature is essentially random. In this work, the scattered signal is modeled as the sum of incoherent contributions arising from single spatial regions that correspond to delay-Doppler cells in the glistening zone. The statistical model of the signal scattered by a moving sea surface is derived by introducing a process for appearing and disappearing of local scattering cores on the sea surface. The scatterers lifetime is governed by a birth–death–immigration process; in the limit of a large number of scatterers, a compound Gaussian process arises, whose temporal statistics are investigated. The analysis of the signal correlation and of the correlation time is considered for the case of an airborne receiver.

This work develops around the problem of modeling statistics and correlation properties of the GNSS L-band signal scattered by the moving ocean surface, presented in the previous paper [1]. The modeling work is here completed by deriving an effective method for simulation of the received signal as well as by making a validation study based on simulated and real data. The simulation of the stochastic process embedding the design statistical properties, the temporal correlation model and the system and surface parameters is intricate, in general, and provides a useful tool for researchers interested in modeling and simulation of GNSS reflected signals from a time evolving sea surface. The GNSS received waveforms after 1 ms coherent integration are simulated and the correlation properties of the specular and near-specular points are compared to the theoretical model and to results from real data. Real raw data were collected during an aircraft mission in the Gulf of Finland and in a spaceborne scenario with CYGNSS satellites. Comparisons show good agreement among simulations, model and real data, and demonstrate that the estimated correlation time of the GNSS-R signal, fits real data with higher accuracy with respect to the case in which sea correlation is not accounted, especially when the receiving platform velocity is moderate.

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 sensitivity of remote sensing instruments for measuring inundation extent can vary widely. Many sensors are suitable for accurate delineation of open water extent, but in vegetated environments the vegetation canopy can obscure the presence of standing water from detection. Detecting inundation extent in these vegetated environments is especially critical for identifying flooding extent where excess surface water extends into the forests surrounding lakes and streams. In addition, cloud cover can impede timely acquisition of imagery by optical sensors. Here, we examine sensitivity of L-band Global Navigation Satellite Systems Reflectometry (GNSS-R) to flooded conditions relative to the well-known signatures of inundation by L-band SAR, and confirm that there is noticeable sensitivity of GNSS reflected signal to inundated areas, including wetlands covered by vegetation, captured by the strong response of the specular reflection by the underlying water surface.

GNSS Reflectometry (GNSS-R) measurements are very sensitive to the presence of inland waters such as wetlands, floods, rivers and lakes. This paper reviews the basic characteristics of a GNSS-R ‘water detection’ research product, including resolution and temporal sampling of wetlands, and discusses the main known sources of errors. Additionally, a summary of GNSS-R applicability to the study of lakes is provided.

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title “Standard for Global Navigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content”. A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grss-ieee.org/community/technical-committees/standards-or-earth-observations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.

The enduring activity of CYGNSS and TDS-1 observatories has provided clear evidence that ground reflected GNSS signal can be exploited for mapping of geophysical quantities in climate and global monitoring applications. One main challenge is to find out useful variables for determining the level of coherence of the scattered wave in the presence of complex natural landscapes such as water basins, river floods, mixed ice and water. In this study, we approach the concept of wave coherence by analyzing the persistence of signal energy across principal directions, determined via generalized eigenvalue decomposition of the DDM delay waveform correlation matrix. An example shows that the dimensional spread of the eigenvalues is finely sensitive to coherence of the ground reflected wave.

Sensitivity of delay-Doppler maps to wind direction is assessed after a deconvolution operation based on a constrained least squares algorithm. The wind direction is estimated using a first-order Taylor expansion of the scattered power function, which leads to an over-determined linear system of equations that is solved with an LS approach. The performance are evaluated under ideal conditions and the sensitivity of the algorithm to wind direction variations is shown.

Recent studies have demonstrated that Global Navigation Satellite System-Reflectometry (GNSS-R) can be used for global wind-speed measurements over the ocean. Delay-Doppler Maps (DDM) are produced continuously by CYGNSS observatories are currently used for wind speed retrievals either directly or after calculation of global observables. The work presented in this paper shows that fine results can be obtained using the scattered power function observable. Experiments are based on real DDMs collected by the space CYGNSS observatories and by ground truth data processed within the collaborative NASA CYGNSS science team.

Ocean GNSS-R is successfully being employed to retrieve wind speed from GNSS signals scattered by the sea surface. To obtain a sufficient scattered field intensity from the ocean surface, the receiver acquires data when it is located along the specular reflection direction. However, new applications of GNSS-R are being explored, among which ship-detection. In this case, scattering from the ocean represents the clutter to be suppressed, so that a different geometry, where the GNSS signals are received in backscattering configuration, is preferable to the forward scattering one. In this new geometry, the Geometric Optics, usually employed in the GNSS-R scientific community, is often no more appropriate to model scattering from the sea surface, and different scattering models must be used. To this aim, we here introduce the Polarimetric Two-Scale Model to evaluate the intensity of the GNSS backscattered signal.

The main purpose of this study is to propose and validate a new algorithm for wind speed retrieval over the ocean using Global Navigation Satellite System-Reflectometry (GNSS-R). The proposed method is based on a Least Squares (LS) estimation from the volume of the scattered power function. Real DDMs collected by the space GNSS receiver-remote sensing instrument (SGR-ReSI) on board the TechDemoSat-1 (TDS-1) satellite and the Advanced SCATterometer (ASCAT) wind speed measurements are used for validation.

The purpose of this paper is to extend the stationary stochastic model defined in [1] to a time evolving sea state and platform motion. The temporal analysis is carried out defining the time-varying model for the signal at the correlator output and its autocorrelation function (acf), following the work by You and Garrison [2, 3]. The temporal evolution of the signal has been integrated into the simulator developed by the Remote Sensing and Telecommunication Research Group of the University of Sannio in order to generate an arbitrary number of istantaneous correlated delay-Doppler maps. The acf function has been estimated by simulation runs and compared to known models, showing very good agreement for the signal scattered from the nominal specular point and from the other bins. The theoretical and estimated decorrelation times are about 4 ms in the nominal specular point. This result is coherent with other estimates obtained in airborne campaigns reported in [2], [4] and simulations reported in Garrison [3].

In this paper the Two-Scale Model has been used to derive the theoretical Normalized Radar Cross Section (NRCS) for sea clutter in the L-band, taking into account also the circular polarization of GPS signals. Using this theoretical model and the theoretical formula for NRCS, authors aim to investigate the possibility to extend target detection through GPS signals in backscattering configuration by varying the incidence angle and the wind speed, whereas results obtained previously were related only to a single value of the incidence angle and to two values of the wind speed. Target Scattered Power and Sea Clutter Power are derived and compared finding that: 1) smaller targets can be detected as the incidence angle increases; 2) the Sea Clutter Power is lower than the one estimated when the “worst case” of a VV-polarization signal is chosen to derive the NRCS value from experimental data. Some final considerations are made as future work.

A stochastic model for delay-Doppler map (DDM) simulation from global navigation satellite system reflectometry (GNSS-R) systems is presented. The aim is to provide a useful tool for investigating the performance of estimation and retrieval algorithms that are based on finite time series. The scattering inside a delay-Doppler cell is modeled as the sum of a random number of contributions from inner specular points that, as the mean number of such contributions gets larger, tends to a compound-Gaussian process. The statistical averages reveal that the model is fully consistent with the previous results provided by Zavorotny and Voronovich. Numerical simulations of large airborne and spaceborne DDMs are easily practicable and show the clear patterns due to signal fluctuations and thermal noise that fade away when the number of averaged observations increases. Comparisons with TechDemoSat-1 data show that the model and the simulation scheme provide accurate realizations of the onboard-processed DDMs.

A new approach for simulation of delay-Doppler maps for ocean global navigation satellite system reflectometry is presented. The simulator is based on a stochastic forward scattering model for generation of realizations of the sea surface reflected signal and allows a flexible definition of system and environmental parameters. The signal scattered from a delay Doppler cell is modelled as the coherent random sum of a large number of components from specular points; the sum converges to a compound-Gaussian random variable when the mean number of points is sufficiently large. In the proposed simulation process, the discretization of the DDM arises from a natural sampling of the delay-Doppler domain rather than on sampling the ocean surface. Instantaneous DDMs are simulated and consistency with the theoretical model is verified.

In this work, authors outline how measurements from different types of sensors can be put together to characterize in an extensive way a sensitive waste dump site. Terrestrial surveying systems and aerial images have been combined with satellite remote sensing techniques to get a complete overview of the surface stability and its thermal characteristics in conjunction with geomorphological conditions and water distribution. The objective has been to implement a broad monitoring system to control the area under observation. Interesting observations have been deducted from the comparison among different data since surface temperature retrievals and variations can be correlated to soil moisture and water exchange by showing that the proposed monitoring system performs correctly in taking under control deformation, terrain displacements, temperature and water surface modifications.

The Nadir reflectances, measured by the SCIAMACHY hyperspectral sensor, are used to retrieve vertical column concentrations of nitrogen dioxide (NO2), resulting from anthropogenic pollution. The estimation process is realized via a blind source separation method: the unmixing of the NO2spectral waveform from the overall atmospheric absorption contribution within the logarithmic reflectance spectra is realized.

The GNSS-R concept is a well-known spaceborne remote sensing technique that, by exploiting GNSS signals reflected from the earth, allows performing simultaneous observations of the Earth on several points over a very wide swath, very promising for applications such as mesoscale altimetry. However, for a single observation on earth, due to the intrinsic low power transmitted by the GNSS satellites, this technique provides reduced performance when compared to conventional spaceborne radars. In order to overcome this limitation, an innovative processing and retracking concept for GNSS-R altimeters based on the acquisition of the full delay-Doppler Map has been recently proposed by the authors. This differs with respect to the conventional GNSS-R processing technique, which exploits only the zero-Doppler power waveform of the DDM. The proposed processing adapts the SAR delay-Doppler concept of spaceborne radar altimeters for use in a GNSS-R system. This processing yields additional multi-look with respect to conventional GNSS-R concepts, and translates into an improvement of the altimetry performance estimated to be up to 35% for practical cases with low SNR. The present paper, after a brief introduction of the delay-Doppler altimetry processing, focuses on the performance analysis of this technique when applied to the for the reference case of the proposed GNSS-R GEROS-ISS experiment to be embarked on the International Space Station.

Atmospheric concentrations of trace-gases are retrieved from hyperspectral data using a blind source separation method. The algorithm relies on the assumption that the absorption cross sections of the gas components are weakly dependent on the overall atmospheric background. The unmixing of contributions from the logarithm of the spectral reflectance provides estimates of both individual trace-gas absorption cross sections and their concentrations. In the experimental analysis, nadir reflectances received by SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY are considered in two scenarios: the sulfur dioxide emissions from a volcanic eruption and the nitrogen dioxide production from anthropogenic pollution. In both cases, it is demonstrated that the algorithm performs very similarly to the Differential Optical Absorption Spectroscopy algorithm but with very little ancillary information.

We propose a new stochastic model governing bistatic returns from GNSS-R systems. The main mechanism exploited in the model is the scattering from specular points and the fluctuations of their number. The scattering inside a delay-Doppler cell is modelled as the sum of a random number of contribution from specular points that, when the number of such contribution is large, converges to a compound-Gaussian process. This process is simple to simulate and is able to model both the spatial and temporal dynamics of the sea surface. After simulation of the waveform received by the receiver, averaging over long time sequences produces results that fully resembles the theoretical results predicted by the Zavorotny and Voronovich model [1].

The average density of specular points plays an important role in the investigation of the sea surface features with remote sensing systems. Evidently, the number of specular reflectors has a direct physical link with the surface roughness that is, in turn, modified by the wind velocity, by the presence of internal waves, oil films and geometric modifications of the sea surface. The main attempt of this paper is to provide an accurate model for the statistics of the number of wind-induced specular points on the sea surface whose spatial correlation properties are provided by the Elfouhaily sea-spectrum model and the temporal correlation is obtaind by introducing a birth-death-immigration process for the number of specular points. Although the model can be also considered for other applications, it is here applied to GNSS-reflectometry where the small scale roughness can be neglected and time decorrelation is given by the motion of the sea waves and by the navigation of the satellite platforms.

A new approach for the retrieval of the vertical column concentrations of trace gases, from hyperspectral satellite reflectances, is presented. The investigation moves from the general rationale of independent component analysis, but the constraint of perfect independence among sources is replaced by a minimum dependence concept that proves more reasonable for the application at hand. The unmixing of the gas spectra and their concentrations is achieved from linear mixtures obtained from the logarithm of the spectral reflectance. After a proper preprocessing stage aimed at reducing major residual dependences caused by atmospheric scattering, trace-gas retrieval is carried out through a minimization of a statistical cost function, subject to the physical constraint that the resulting spectra must be nonnegative. The experimental analysis relies on the retrieval of sulfur dioxide during volcanic emissions using data from the National Aeronautics and Space Administration Ozone Monitoring Instrument. To validate the procedure, reference reflectance spectra having a known profile of sulfur dioxide are generated with the MODerate resolution atmospheric TRANsmission software, and the retrieved concentration is compared with the theoretical one. Performance in the presence of shot and detector noise has also been analyzed starting from pure simulated spectral reflectances.

We present the implementation of a facet-based simulator to investigate the forward scattering of L-band signals from realistic sea surfaces and its application to spaceborne ocean Global Navigation Satellite System (GNSS) Reflectometry. This approach provides a new flexible tool to assess the influence of the ocean surface roughness on scattered GNSS signals. The motivation stems from the study by Clarizia, which revealed significant differences between delay–Doppler maps (DDMs) obtained from UK-DMC satellite data and DDMs simulated with the Zavorotny–Voronovich (Z-V) model. Here, the scattered power and polarization ratio (PR) are computed for explicit 3-D ocean wave fields, using a novel implementation of the Kirchhoff approximation (KA), which we call the Facet Approach (FA). We find that the FA is consistent with the full KA and the Geometrical Optics (GO) used in the Z-V model, while being less computationally expensive than the KA and able to represent polarization effects not captured by the GO. Instantaneous maps of the bistatic normalized radar cross section computed with the FA show clear patterns associated with the underlying waves. The wave field is particularly visible in the PR, indicating that the scattering is generally dominated by the HH component, particularly from ocean wave troughs. Polarization effects show, for the first time, a strong correlation to the explicit sea surface from which the scattering originated. DDMs of the scattered power computed with the FA reveal patchy patterns and power distributions that differ from those obtained with Z-V and show closer similarities with observed DDMs from UK-DMC.

A high-contrast inpainting scheme, based on the Complex Ginzburg-Landau equation, is successfully applied to restoration of SAR interferograms. The algorithm demonstrates quite effective in recovering the phase values in low coherence regions. The validation setup has been carried out in the presence of additive phase noise with a suitable probability density function. Results show that the application of the proposed algorithm to small regions produces very good results both in terms of Signal-to-Noise Ratio (SNR) and in terms of Mean Square Error (MSE).

A new approach is proposed for the retrieval of trace gases, vertical column concentrations, from hyperspectral satellite observations. This technique consists of a semi-blind recovery of concentrations and pure spectra from their linear mixtures. This decomposition is based on the Least Dependent component Analysis (LDA) technique, a method that generalizes the well known Independent Component Analysis, to find least dependent components, after a proper preprocessing for reducing some residual dependencies. Some results are shown for the retrieval of sulphur dioxide SO2volcano emission using data from the NASA Ozone Monitoring Instrument (OMI).

Significant improvements on the detection of thermal anomalies in multispectral satellite data can be obtained when both the false alarm rate and the probability of detection are known. A desirable, optimum system should have constant false alarm rate and maximum probability of detection. While proper hypotheses can be done on the background statistical distribution, on target for constant false alarm rate, a statistical model for thermal anomalies is not easily available, and, consequently, the detection probability is not available in a closed form. Therefore, an appropriate description of the physics of the phenomenon is required: the mixed pixel model provides a valid answer to this need. Once a physical model is available, a Monte Carlo simulation can be performed for evaluating the probability of detection. Simulated data of thermal anomalies have been compared with the fire pixel temperatures from NASA-DAAC MOD14 showing the good agreement between simulated and experimental data. Finally, the receiver operating characteristic of a constant false alarm rate system has been derived through simulation, and comparisons between optimum and non-optimum systems as well as between systems with different rules for the channels fusion have been carried out.

A high-contrast inpainting scheme based on the Complex Ginzburg-Landau equation recently applied successfully to image restoration is applied to SAR interferograms to improve their quality and therefore final quality of Digital Elevation Models (DEMs). The new technique attempts to recover the phase values in low coherence regions through diffusion and inpainting. After phase unwrapping low coherence regions are masked and discarded and a Complex Ginzburg-Landau (CGL) inpainting scheme is applied to regions where phase values are missing. We demonstrate that the residues reduce and the proposed algorithm leads to a higher Signal-to-Noise Ratio (SNR) if compared with MCF algorithm. The restoration technique has been applied to ERS-1 and ERS-2 data sets acquired on July 1995. Results appear to be very promising: the proposed algorithm provides good performances especially in presence of strong noise level and low coherence areas with relatively small dimensions.

We propose a new method for trace gases concentration using satellite measurements. The main idea is to use the full retrieved waveform through a blind source separation algorithm, instead of processing differential observation taken from a few absorption lines. The main assumption in this work is that the spectral waveforms from atmosphere contributions can be considered statistically independent. Results show that the algorithm is able to operate quite good although some unwanted features from clouds are clearly observed.

This paper deals with the problem of Constant False Alarm Rate (CFAR) detection of thermal anomalies in multispectral satellite data. The goal is to provide robustness to the algorithm proposed in [1], with respect to the presence of outliers in the analysis window. In [1], data from 4 μm and 11 μm MODIS bands, that are statistically correlated, are re-projected through a Principal Component Analysis (PCA) to obtain uncorrelated data, a necessary condition for the final stage of CFAR detection. Unfortunately, the sample covari-ance matrix used in the PCA can be strongly affected by the presence of thermal anomalies, therefore a robust estimator is needed. To this aim, the Minimum Covariance Determinant estimator (MCD) is introduced in the PCA yielding an analysis that is little influenced by the presence of anomalies while provides results similar to the usual PCA for uncontaminated data. Experimental results have shown that many detections can be missed if the MCD estimator is not used in the presence of anomalies, even if their number is not so high but their values are able to modify significantly the sample covariance matrix. The robust multiband CFAR algorithm has been applied to a MODIS image and results have been compared with those from NASA-DAAC MOD14.

A new method for precise registration of satellite images in the presence of local distortions is presented. The template matching is based on Cumulative Residual Entropy (CRE) of the difference-image, a new and powerful similarity measure. The regularization process relies on inpainting, technique that has demonstrated eccellent results in frame interpolation or in improving sparsely sampled data. The CRE measure is simple to estimate, robust to noise and local deformations, invariant to amplitude scaling, more efficient on small sample data than other similarity measures such as mutual information and cross-correlation. An outliers detection method is applied to locate potential errors, that are rejected and corrected with the inpainting algorithm. Experimental results on simulated images have shown quantitatively the accuracy of the proposed method.

Global Navigation Satellite System Reflectometry (GNSS-R) is a new approach for earth observation using signals of opportunity in a bistatic configuration. The system, in the configuration of interest, exploits the 10 bits PN sequence of the GPS system to generate Delay/Doppler maps that are useful for monitoring the sea state. One of the open problems is that the achievable delay and Doppler resolution is limited by the GPS waveform. We will show that, exploiting a MUSIC-based algorithm, it is possible to move further the limits for the achievable delay and Doppler resolution.

We present a new approach to the retrieval of sea surface roughness using GNSS-R. The steps through the simulation of the whole end-to-end microwave scattering of GNSS signals from the sea surface are explained, with emphasis on how to generate a linear sea surface and to implement the Kirchhoff Approximation (KA), as the large-scale part of the full scattering model. We illustrate some examples of radar cross sections calculated using the Kirchhoff scattering model, and how they change with respect to different polarizations. Their variations with geometry, sea state and spatial resolution are investigated and discussed.

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 theout-of-familydetectors, 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.

An image registration algorithm based on mutual information maximization and non-linear diffusion is presented. It relies on a non-parametric estimation of the degree of dependency between the reference image and the template to be registered, which is intrinsically more robust against possible deformations due to imaging geometry and propagation disturbances. The approach based on non linear diffusion, nonetheless, has the advantage of producing a non-parametric discrete warping model which does not rely on a particular set of basis functions, and is therefore as much general as possible. The experimental results on simulated images have quantitatively shown the accuracy of the proposed method.

This paper deals with the problem of CFAR detection of thermal anomalies in multispectral satellite data. The goal is to extend the algorithm proposed in [1], and successfully applied to MODIS data from band 21, to the case of multiband investigation. A multiple-channel model has been designed, where data from MODIS bands 21 and 31 are projected into a new coordinates system by adopting the Principal Component Analysis (PCA). A preliminary statistical analysis has been performed on both the principal components of data to verify that the Weibull distribution can be adopted for background. Subsequently, a Kendall test has been used to check the level of dependency of the projected data and it has shown that channels independence can be assumed with high significance level. After PCA, a CFAR detection is applied to projected data and thanks to data independence the single detections are combined with an AND rule. The outcome of the AND operation gives the thermal anomalies detected in both channels with an assigned overall probability of false alarm (PFA). The Multiband CFAR algorithm has been applied to a 256 × 256 MODIS image from bands 21 and 31 and results have been compared with those from NASA-DAAC MOD14.

The atmospheric phase screen is one of the main error sources that affect the precise phase measurements in many fields of earth remote sensing. The atmospheric effects can be mitigated if a precise knowledge of the power spectral density of the process is available and if same sample observations can be retrieved on a sparse grid. At smaller scales, where interactions are no longer isotropic, the behaviour is not easily predicted by the ultimate dissipative behaviour of turbulence eddies. We start by assuming that the propagation of the electromagnetic wave in the lower atmosphere can be represented by a ray travelling in a layered medium where the refractive index is constant along each layer. In a turbulent atmosphere, the interaction among eddies induces a diffusion process that propagates with different rates in both vertical and horizontal direction with the final effect of ruling the number of effective layers in the atmosphere. In this way, the overall path travelled by the electromagnetic wave is governed by the accumulated number of such effective layers whose interactions play a primary role in our model. A good model for the interactions among different layers is the linear interaction model. The power spectrum of the process can be found by solving a differential equation with given initial conditions. It can be demonstrated that an asymptotic power law decay is found under binomial competitive interactions and that, at a smaller scale, the behaviour observed in the observed data is naturally induced by the interaction process itself. The model predictions have been tested using samples of the atmospheric phase screen extracted from Synthetic Aperture Radar interferograms. To this end, the model parameters have been estimated from the data set and the predicted spectrum has been compared with the measured one.

Abstract — This paper introduces the concept of Constant False Alarm Rate (CFAR) in fire detection for multispectral satellite data. A new algorithm is proposed, based on a technique successfully applied for detection of extended objects in High Resolution SAR images. It compares the pixel under analysis with an adaptive threshold, suitably estimated from the pixels surrounding the one under test, in order to ensure the CFAR property. The proposed approach requires that the background distribution is of Location Scale (LS) type or amenable to such a distribution by a suitable transformation. MODIS data from the 4 µm channel are considered. A preliminary statistical analysis is performed to verify if the Weibull distribution, compliant with LS representation, can be adopted for background. MODIS cloud and water masking are applied to identify those pixels to be discarded before implementing the statistical analysis. Results of fire detection are presented for different values of the system parameters (censoring depth and false alarm rate) and compared with the algorithm implemented in the NASA-DAAC MOD14. I.

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.

In this paper, we present a new destriping algorithm for MODIS images. The algorithm takes advantage of the observation redundancy due to the bow-tie effect. It estimates the equalization curve from single fields of view observed by multiple detectors. Results show that the strategy is correct in those regions where equalization curve is estimated in the low to moderate range of radiance

This paper deals with the synthesis and the analysis of optimum receivers to detect one out of M equally likely, equi-energy, fading signals in impulsive noise, modelled as a compound Gaussian, possibly correlated process. We show that the conventional coherent and incoherent detectors are still optimum, independent of the noise as well as the fading probability density functions. The performance analysis has been carried on with reference to the general case of arbitrarily distributed disturbance: in order to simplify the analysis, asymptotical expressions have been developed for high signal-to-noise ratios as well as high signal space dimensionality. Interestingly enough, this allows separating the effect of the noise spikyness from that of the fading law. Results indicate that, for deep fading, the noise marginal distribution does not dramatically affect the error probability, nor is it influential on the limit operating characteristics corresponding to infinite signal space dimensions. For non-fluctuating signals, instead, the noise distribution plays a primary role: spiky noise usually produces performance impairment; moreover, the limit performance in impulsive disturbance may exhibit marked deviations from the well-known stepwise shape which is typical of Gaussian channels.>

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.>