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The Terra and Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) scan mirror reflectance is a function of the angle of incidence (AOI) and was characterized prior to launch by the instrument vendor. The relative change of the prelaunch response versus scan angle (RVS) is tracked and linearly scaled on-orbit using observations at two AOIs of 11.2° and 50.2° corresponding to the moon view and solar diffuser, respectively. As the missions continue to operate well beyond their design life of six years, the assumption of linear scaling between the two AOIs is known to be inadequate in accurately characterizing the RVS, particularly at short wavelengths. Consequently, an enhanced approach of supplementing the on-board measurements with response trends from desert pseudoinvariant calibration sites (PICS) was formulated in MODIS Collection 6 (C6). An underlying assumption for the continued effectiveness of this approach is the long-term (multiyear) and short-term (month to month) stability of the PICS. Previous work has shown that the deep convective clouds (DCC) can also be used to monitor the onorbit RVS performance with less trend uncertainties compared with desert sites. In this paper, the raw sensor response to the DCC is used to characterize the on-orbit RVS on a band and mirror-side basis. These DCC-based RVS results are compared with those of C6 PICS-based RVS, showing an agreement within 2% observed in most cases. The pros and cons of using a DCC-based RVS approach are also discussed in this paper. Although this reaffirms the efficacy of the C6 PICS-based RVS, the DCC-based RVS approach presents itself as an effective alternative for future considerations. Potential applications of this approach to other instruments, such as Suomi National Polarorbiting Partnership, Joint Polar Satellite Systems, and Visible Infrared Imaging Radiometer Suite, are also discussed. [ABSTRACT FROM AUTHOR]

Supervised approaches classify input data using a set of representative samples for each class, known as training samples. The collection of such samples is expensive and time demanding. Hence, unsupervised feature learning, which has a quick access to arbitrary amounts of unlabeled data, is conceptually of high interest. In this paper, we propose a novel network architecture, fully Conv-Deconv network, for unsupervised spectral-spatial feature learning of hyperspectral images, which is able to be trained in an end-to-end manner. Specifically, our network is based on the so-called encoder-decoder paradigm, i.e., the input 3-D hyperspectral patch is first transformed into a typically lower dimensional space via a convolutional subnetwork (encoder), and then expanded to reproduce the initial data by a deconvolutional subnetwork (decoder). However, during the experiment, we found that such a network is not easy to be optimized. To address this problem, we refine the proposed network architecture by incorporating: 1) residual learning and 2) a new unpooling operation that can use memorized max-pooling indexes. Moreover, to understand the "black box," we make an in-depth study of the learned feature maps in the experimental analysis. A very interesting discovery is that some specific "neurons" in the first residual block of the proposed network own good description power for semantic visual patterns in the object level, which provide an opportunity to achieve "free" object detection. This paper, for the first time in the remote sensing community, proposes an end-to-end fully Conv-Deconv network for unsupervised spectral-spatial feature learning. Moreover, this paper also introduces an in-depth investigation of learned features. Experimental results on two widely used hyperspectral data, Indian Pines and Pavia University, demonstrate competitive performance obtained by the proposed methodology compared with other studied approaches. [ABSTRACT FROM AUTHOR]

In this paper, the sensitivity and detectability of an electromagnetic induction tool for hydraulic fracture detection are analyzed by calculating the electromagnetic response from an orthogonal transverse fracture (OTF) in an open or cased borehole using the improved numerical mode matching method. The OTF is modeled as a slim circular disk, which is axially symmetric with respect to the borehole axis and filled with a conductive or magnetic proppant. The feasibility of the induction tool for fracture diagnostics was validated via scaled-down experimental measurements. We then quantitatively analyze the induction response sensitivity to different proppant parameters, such as conductivity and permeability, to evaluate the effectiveness of the induction logging tool for fracture detection. The analysis reveals that the short-spaced receiver can accurately locate the fracture position and distinguish small-sized fractures, while the long-spaced receiver can differentiate fractures with large dimension. Casings with different thicknesses, conductivities, and permeabilities are modeled together with the fracture to investigate their effects in fracture detection. Finally, the application of the induction tool to a fracture network is evaluated. [ABSTRACT FROM AUTHOR]

The Vostok ice core provides measurements of the CO2 concentration during the last 414 x 10³ years (yr). Estimations of power spectra show peaks, with the strongest one corresponding to a time period of around 100 x 10³ yr. In this paper, a new reconstruction method from irregular sampling is used, allowing more accurate estimation of spectral peaks. This method intrinsically decomposes the analyzed signal as a sum of sines, providing amplitudes but also phase measurements of periodic tendencies (due to the nature of the studied phenomena). This decomposition can be conducted with noisy and inaccurate measurements of the sampling instants and the concentrations. The widely used Vostok data were chosen as an example, but the method could also be applied to data from other places (e.g., dome C, Antarctica) or to study other phenomena as nitrogen dioxide NO2, methane CH4, oxygen isotope 18O (closely linked to temperature), deuterium ²H, or dust concentrations. [ABSTRACT FROM AUTHOR]

This paper describes a new model of the seawater dielectric constant as a function of salinity and temperature at L-band. The model function is developed by fitting the accurate measurement data made at 1.413 GHz to a third-order polynomial. The purpose of this study is to provide an accurate model for earth-observing satellites to retrieve seawater salinities from remote sensing data. In this paper, the development of the model function is introduced along with an analysis of the goodness of fit. The model function is then compared with the model functions of Klein-Swift and Meissner-Wentz. Finally, the comparison is made between the retrieved salinity from the satellite data with the in situ data measured by Argo floats. [ABSTRACT FROM PUBLISHER]

The automatic detection of buried threats in ground penetrating radar (GPR) data is an active area of research due to GPR's ability to detect both metal and nonmetal subsurface objects. Recent work on algorithms designed to distinguish between threats and nonthreats in GPR data has utilized computer vision methods to advance the state-of-the-art detection and discrimination performance. Feature extractors, or descriptors, from the computer vision literature have exhibited excellent performance in representing 2-D GPR image patches and allow for robust classification of threats from nonthreats. This paper aims to perform a broad study of feature extraction methods in order to identify characteristics that lead to improved classification performance under controlled conditions. The results presented in this paper show that gradient-based features, such as the edge histogram descriptor and the scale invariant feature transform, provide the most robust performance across a large and varied data set. These results indicate that various techniques from the computer vision literature can be successfully applied to target detection in GPR data and that more advanced techniques from the computer vision literature may provide further performance improvements. [ABSTRACT FROM PUBLISHER]

Scattering from rice leaves contributes substantially to total vegetation canopy backscattering and detailed knowledge about it is necessary for developing a microwave scattering model. A parabolic curve is generally adopted to simulate the leaf shape but this is rarely incorporated into the calculation of the scattering. In this paper, two specific models, one based on physical optics (PO) approximation and the other on the discrete dipole approximation (DDA), are presented to involve the parabolic leaf curvature effects. Three typical leaves were chosen from 1433 parabolic leaves obtained during ground measurements. The PO and DDA models were used to calculate the leaf scattering. The generalized Rayleigh–Gans (GRG) approximation was also included in the simulation. The method of moments, a computational electromagnetic method, was utilized to evaluate the accuracy of each model. Validation of the models was conducted at incidence angles ranging from 10° to 60°, incidence azimuthal angles ranging from 0° to 360°, and incidence frequencies of 1.2 GHz (L-band), 5.4 GHz (C-band), and 9.65 GHz (X-band). Among the GRG approximation, the DDA model and the PO model, the latter gave the best accuracy −>65% in the cases tested, while the GRG model was the least accurate. The high accuracy of the PO model was maintained at both the low and high frequency bands. The PO model, therefore, has great potential for use to interpret radar measurements from rice fields and other types of vegetation canopy. [ABSTRACT FROM PUBLISHER]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

Processing large very high-resolution remote sensing images on resource-constrained devices is a challenging task because of the large size of these data sets. For applications such as environmental monitoring or natural resources management, complex algorithms have to be used to extract information from the images. The memory required to store the images and the data structures of such algorithms may be very high (hundreds of gigabytes) and therefore leads to unfeasibility on commonly available computers. Segmentation algorithms constitute an essential step for the extraction of objects of interest in a scene and will be the topic of the investigation in this paper. The objective of the present work is to adapt image segmentation algorithms for large amounts of data. To overcome the memory issue, large images are usually divided into smaller image tiles, which are processed independently. Region-merging algorithms do not cope well with image tiling since artifacts are present on the tile edges in the final result due to the incoherencies of the regions across the tiles. In this paper, we propose a scalable tile-based framework for region-merging algorithms to segment large images, while ensuring identical results, with respect to processing the whole image at once. We introduce the original concept of the stability margin for a tile. It allows ensuring identical results to those obtained if the whole image had been segmented without tiling. Finally, we discuss the benefits of this framework and demonstrate the scalability of this approach by applying it to real large images. [ABSTRACT FROM PUBLISHER]

This paper presents the numerical method, generalized propagation-inside-layer expansion (GPILE), to calculate the scattered electromagnetic field by any stratified 1-D medium composed of three random rough interfaces separating homogeneous media. GPILE is a generalization of the propagation-inside-layer expansion method, which considers only two interfaces. Both methods rely on the rigorous implementation of the Maxwell equations, with a simple matrix formulation and which have a straightforward physical interpretation. In particular, this method allows us to distinguish the primary echo of the upper surface and also the multiple echoes arising from the intermediate and lower interfaces. This method is applied in this paper to simulate the ground-penetrating radar (GPR) signal at nadir. The simulated signals are analyzed to study the sensitivity of the GPR signal to any material debonding within the pavement layered structure. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a novel multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) concept for multimodal operation. The proposed system employs waveforms based on the orthogonal frequency division multiplexing (OFDM) technique and digital beamforming (DBF) on receive. Thereby, it becomes feasible to maximize spatial degrees of freedom, which are necessary for the multimodal operation. The proposed MIMO SAR system produces multiple high-resolution wide-swath SAR imageries that are used for coherent postprocessing. Through this paper, we aim to open up a new perspective of using MIMO concept for a wide-swath SAR imaging with high resolution in interferometric and polarimetric modes, based on OFDM and DBF techniques. Therefore, this paper encompasses broad theoretical backgrounds and general system design issues as well as specific signal processing techniques and aspects. [ABSTRACT FROM AUTHOR]

This paper demonstrates that the finite-difference time-domain (FDTD) method can be used to accurately simulate ground-penetrating radar (GPR) wave and its interaction with pavement having different densities and moisture conditions. The FDTD simulation described in this paper is part of the research efforts into the application of GPR on monitoring the density of asphalt pavement during compaction. The challenge is to separate the effect of density increase from the effect of the increase in surface moisture content on GPR signals. The first part of this paper describes the procedure for finding an equivalent excitation function used in simulation that can generate a GPR signal that matches the real signal. Discrete wavelet transform (DWT) was applied to denoise the signal. It was found that the equivalent excitation source and the DWT technique provided an accurate match between the simulation signal and real GPR signal in both the time domain and frequency domain. The second part of this paper demonstrates the application of FDTD simulation in investigating GPR responses on pavements with different densities and surface moisture content. The simulation successfully revealed the “frequency selective” feature of the effect of surface moisture content on GPR signals. Laboratory experiments were also conducted, and the results verified the findings in FDTD simulations. The FDTD simulation was shown to be an effective and efficient tool for studying the interaction of GPR waves with pavement. An accurate modeling of the signal and its propagation provided meaningful insights as well as the possibility for developing practical data-processing techniques. [ABSTRACT FROM PUBLISHER]

The field of wireless communications has benefited from multiple-input and multiple-output (MIMO) techniques. As researchers seek to apply MIMO (multistatic) techniques to radar and specifically to synthetic aperture radar (SAR), a key factor in determining MIMO application and performance is the level of correlation of signals from different receiver/transmitter pairs. The level of correlation determines whether a MIMO array falls into the category of a collocated array or a distributed array. The type of array dramatically affects which MIMO techniques may be performed and what advantages MIMO offers from conventional techniques. This paper presents models for calculating geometric correlation of multistatic SAR pixels using a ground-plane image formation. The models' results are compared to previous correlation models found in literature. A key result is that correlation depends on pixel resolution and not the number of individual scatterers. This paper concludes that most MIMO arrays operating on a single platform operate in the collocated regime. [ABSTRACT FROM PUBLISHER]

In this paper, the defocus of a 2-D image reconstruction of targets illuminated by Gaussian beams in terahertz (THz) single-frequency holography was studied. An analytical point-spread function was derived to quantitatively evaluate the defocus effect, considering the deviation of the restoration distance from the real distance of the target. It is found that the cross-range image defocuses in a similar way to the diffusion of a Gaussian beam from its beam waist, which limits the depth of focus for image restoration. To improve the restored images for targets with varying range distances, an adaptive focusing algorithm based on the minimum-entropy method was proposed for single-frequency holography. Simulation results with fairly good agreement were performed to verify the theoretical results and the proposed algorithm. Proof-of-principle experiments in the 0.2-THz band were also performed based on a monostatic prototype imager with a Gaussian beam transceiver. The experimental results confirm the effectiveness of the adaptive focusing reconstruction algorithm proposed in this paper. [ABSTRACT FROM PUBLISHER]

In this paper, we report the vertical profiles of CO2 and CH4 measured with a cavity ring-down spectrometer (CRDS) on a research aircraft from near-ground level to 8 km above mean sea level. The airborne platform employed in this paper is an Alpha Jet aircraft operated from NASA's Ames Research Center. Flights were undertaken to Railroad Valley, NV, USA, to coincide with overpasses of the Greenhouse Gases Observing Satellite (GOSAT). Ground-based CO2 and CH4 were simultaneously measured using CRDS, at the time and location of the airborne and satellite measurements. Results of three GOSAT coordinated aircraft profiles and ground-based measurements in June 2011 are presented and discussed in this paper. The accuracy of the CO2 and CH4 measurements has been determined based upon laboratory calibrations (World Meteorological Organisation traceable standard) and pressure/temperature flight simulations in a test chamber. The overall uncertainty for the airborne measurements ranged from 0.31 to 0.39 ppm for CO2 and from 3.5 to 5.6 ppb for CH4. Our column-averaged CO2 and CH4 measurements, which include about 61% of the total atmospheric mass, are extrapolated, using different techniques, to include the remainder of the tropospheric and stratospheric CO2 and CH4. The CO2 data are then analyzed using the Atmospheric CO2 Observations from Space 2.9 and 3.3 algorithms. For methane data, the RemoTeC v2.1 algorithm was used in its full physics setup. Column-averaged CO2 and XCO2, measured by GOSAT and analyzed from our data, ranged from 388.1 to 396.4 ppm, and XCH4 ranged from 1.743 to 1.822 ppm. The agreement of the satellite and aircraft CO2 mixing ratios, as well as ground measurements, falls within the uncertainties of the methods employed to acquire these numbers. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

This paper deals with the issue of ground moving target imaging and motion parameter estimation with an airborne dual-channel circular stripmap synthetic aperture radar (CSSAR) system. Although several methods of ground moving target motion parameter estimation have been proposed for the conventional airborne linear stripmap SAR, they cannot be applied to airborne CSSAR because the range history of a ground moving target for airborne CSSAR is different than that for airborne linear stripmap SAR. In this paper, the moving target’s range history for airborne dual-channel CSSAR and the target signal model after the displaced phase center antenna processing are derived, and a new ground moving target imaging and motion parameter estimation algorithm is developed. In this algorithm, the estimation of baseband Doppler centroid and its compensation are first performed. Then focusing is implemented in the 2-D frequency domain via phase multiplication, and the target is focused in the SAR image without azimuth displacement due to the compensation of the Doppler shift caused by its motion. Finally, the target’s motion parameters are estimated with its Doppler parameters and its position in the SAR image. Numerical simulations are conducted to validate the derived range history and the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology to design a spaceborne dual-beam along-track synthetic aperture radar interferometer to retrieve ocean surface velocity vectors. All related aspects and necessary tradeoffs are identified and discussed or reviewed, respectively. This includes a review of the measurement principle and the relation between baseline and sensitivity, the relation between wind and radar backscatter, a discussion of the observation geometry, including the antenna concept, polarization diversity, and all main error contributions. The design methodology consists of a sensitivity-based derivation of explicit instrument requirements from scientific requirements. In turn, this derivation is based on a statistical model for the interferometric phase error. This allows a quantitative, well-grounded instrument design offering an additional degree of freedom to the approach, which we call “noise-equivalent-sigma-zero requirement space.” Crucial tradeoffs for the system design, such as the resolution, the number of independent looks, the minimum wind speed, and the coherence and ambiguities, are pointed out and discussed. Finally, this paper concludes with a single platform system concept operating in Ku-band, which provides the measurement quality needed to achieve a surface velocity estimation accuracy of 5 cm/s, 200-km swath coverage, for 4 $\times$ 4 km2 L2-product resolution and winds starting at 3 m/s. [ABSTRACT FROM PUBLISHER]

In this paper, the authors investigate the statistical matching of the panchromatic (Pan) image to the multispectral (MS) bands, also known as the histogram matching, for the two main classes of pansharpening methods, i.e., those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods. Also, hybrid methods combining CS with MRA, like the widespread additive wavelet luminance proportional (AWLP), are investigated. It is shown that all spectral, spatial, and hybrid methods must perform a dynamics matching of the enhancing Pan to the individual MS bands for MRA or a combination of them (the component that shall be substituted) for CS. For hybrid methods, the problem is more complex and both types of histogram matching may be suitable. Such an intersensor balance may be either explicit or implicitly performed by the detail-injection model, e.g., the popular projective and multiplicative injection models. An experimental setup exploiting IKONOS and WorldView-2 data sets demonstrates that a correct histogram matching is the key to attain extra performance from established methods. As a first result of this paper, the AWLP method has been revisited and its performance significantly improved by simply performing the histogram matching of Pan to the individual MS bands, rather than to the intensity component, thereby losing the original proportionality feature. [ABSTRACT FROM PUBLISHER]

In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of the geometrical properties of hyperspectral signatures. Experimental results on both real and synthetic data sets show that the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mixtures present in the pixels of the scene. [ABSTRACT FROM PUBLISHER]

This paper completes the fundamental development of the basic coherent entities in radar polarimetry for coherent reciprocal scattering involving polarized wave states, antenna states, and scattering matrices. The concept of antenna polarization states as contravariant spinors is validated from fundamental principles in terms of Schelkunoff’s reaction theorem and the Lorentz reciprocity theorem. In the general bistatic case, polarization states of different wavevectors must be related by the linear scattering matrix. It is shown that the relationship can be expressed geometrically, and that each scattering matrix has a unique complex scalar invariant characterizing a homographic mapping relating pairs of transmit/receive states for which the scattering amplitude vanishes. We show how the scalar invariant is related to the properties of the bistatic Huynen fork in both its conventional form and according to a new definition. Results are presented illustrating the invariant $k$ for a range of spheroidal Rayleigh scatterers. [ABSTRACT FROM PUBLISHER]

This paper contributes to the predictive understanding of satellite precipitation estimation errors over complex terrain, which is fundamental to the development of error models for improving hydrological applications. This paper focuses on the Trentino-Alto Adige region of the eastern Italian Alps. Rainfall observations over a 10-year period (2000–2009) from a dense rain gauge network in the region are used as reference precipitation. A number of satellite precipitation error properties (probability of detection, false alarm rates, missed events, spatial correlation of the error, and hit biases) are investigated in terms of seasonality, satellite algorithm, rainfall intensity, gauge density, and temporal resolution dependencies. These error parameters are typically used in error models (e.g., SREM2D) and provide the basis for enhancing error scheme development. Three widely used satellite-based precipitation products are employed: 1) the Climate Prediction Center morphing product; 2) the precipitation estimation from remotely sensed imagery using artificial neural networks; and 3) the Tropical Rainfall Measuring Mission multisatellite precipitation analysis 3B42 near-real-time product. The three products show similar performances, with larger errors during the warm season, characterized by convective storms, and less variability in the cold season, characterized by more organized stratiform systems. Lower biases are depicted at the daily scale with respect to the 3-hourly resolution. The SREM2D error model has the ability to correct the satellite precipitation products, even though attention is needed for potential systematic errors when applying the calibrated model to independent periods or regions. [ABSTRACT FROM PUBLISHER]

The classification accuracy of remote sensing data can be increased by integrating ancillary data provided by multisource acquisition of the same scene. We propose to merge the spectral and spatial content of hyperspectral images (HSIs) with elevation information from light detection and ranging (LiDAR) measurements. In this paper, we propose to fuse the data sets using orthogonal total variation component analysis (OTVCA). Extinction profiles are used to automatically extract spatial and elevation information from HSI and rasterized LiDAR features. The extracted spatial and elevation information is then fused with spectral information using the OTVCA-based feature fusion method to produce the final classification map. The extracted features have high dimension, and therefore OTVCA estimates the fused features in a lower dimensional space. OTVCA also promotes piece-wise smoothness while maintaining the spatial structures. Both attributes are important to provide homogeneous regions in the final classification maps. We benchmark the proposed approach (OTVCA-fusion) with an urban data set captured over an urban area in Houston/USA and a rural region acquired in Trento/Italy. In the experiments, OTVCA-fusion is evaluated using random forest and support vector machine classifiers. Our experiments demonstrate the ability of OTVCA-fusion to produce accurate classification maps while using fewer features compared with other approaches investigated in this paper. [ABSTRACT FROM PUBLISHER]

This is the second of two papers that quantify the high added value of frequent 3-D radar observations of the atmosphere to capture the dynamics of weather systems. Recent advances in small-satellite and radar technologies, such as the “Radar in Cubesat” developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between such observations to the underlying cloud variables and processes, and to quantify the sensitivity to the different physical and instrument parameters. In this paper, we quantify the uncertainty in the relation between the measured radar reflectivities Z and their time derivatives d_{t} Z , on one hand, and the underlying rate of change of the condensed-water mass M$ , and fluxes of dry and moist air in convection, on the other hand. The uncertainties are due to the variability of the atmospheric parameters as well as the constraints of an observation strategy that would use pairs of spaceborne instruments. We specifically analyze the sensitivities for pairs of satellites, each carrying a Ka-band profiling radar. Our simulations show that, with a convoy of two spacecraft separated by ~90 s, each with a pointing accuracy of ~0.025° in rms error, a sensitivity of 17 dBZ and a precision of 1 dBZ, the proposed observation strategy would capture more than 70% of the tropical convection between 5 and 10 km of altitude and resolve the air-mass and condensed-water fluxes. [ABSTRACT FROM AUTHOR]

The AisaOWL is a recent-to-market thermal hyperspectral instrument. As such, there is little information about the sensor performance in the literature. The sensor covers the 7.6– 12.6~\mu \textm part of the long-wave infrared region with 102 continuous bands, and is capable of imaging in low-light conditions. This paper presents an independent characterization of the AisaOWL sensor, examining the spectral accuracy of black body measurements at different temperatures and validating manufacturer recommendations for warm-up, integration, and calibration times. This analysis is essential for establishing high quality operational procedures and in giving confidence to users of the data. In this paper, the sensor has been found to have a maximum error of 2 °C in absolute temperature measurement, and provides spectra most accurate in the 8– 9~\mu \textm region. The recommended warm-up time of 15 min has been confirmed, with a 1% increase in error identified for data collected only 7 min after switch on. The optimal integration time of 1.18 ms has been validated and an exponential decrease in performance observed outside the 0.85–1.2 ms range. The detector used by the sensor is shown to have stability issues and this has been examined by comparing black body data processed with different calibration data. While the detector is operating in a stable regime compatible with the calibration, these black body readings stay within 5% across the central bands, approaching 10% below 8~\mu \textm and just exceeding 20% above 11 \mu \textm . [ABSTRACT FROM AUTHOR]

Automatic registration of multimodal remote sensing data [e.g., optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR)] is a challenging task due to the significant nonlinear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the histogram of orientated phase congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation and use the extended model to build HOPCncc. Then, a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images and has been tested with a variety of optical, LiDAR, SAR, and map data. The results show that HOPCncc is robust against complex nonlinear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration. [ABSTRACT FROM PUBLISHER]

In an effort to study and characterize scattering mechanisms of debris particles in tornadoes, a numerical polarimetric radar emulator was developed. This paper is primarily motivated by attempts to explain radar observations near tornadoes. One such observation is the regions of negative differential reflectivity, which have been found near tornadoes but they are yet to be explained physically. There are hypotheses that suggest common debris alignment and/or dominant scattering from objects with high radar-cross-section (RCS) values that cause negative ZDR , but they are extremely challenging to verify due to the inherent danger near the vicinity of tornadoes. It is, however, possible to numerically construct the scenes through representative simulations to verify the plausible causes. This serves as our primary motivation to develop the radar emulator. The novel aspects of this paper are the realistic trajectory derivation, which is based on a physical air-drag model, and the representative diversity of RCS contributions from each debris object, developed through realistic polarimetric RCS modeling and anechoic chamber measurements. [ABSTRACT FROM PUBLISHER]

In this paper, the development of a polarimetric phased array weather radar, which consists of a dual-polarized antenna with 2-D circular planar phase-array elements, is discussed. The radar is capable of measuring the 3-D rainfall distribution in less than several tens of seconds. Digital beamforming (DBF) is an important component in the development process of the phased array radar. In this paper, precipitation radar signal simulations are performed taking into consideration radar concepts in order to discuss the estimation accuracy of polarimetric precipitation profiles (differential reflectivity, specific differential phase, and copolar correlation coefficient) with two DBF methods that are based on Fourier and minimum mean-square error (MMSE) methods. A comparison of the performance of the two methods indicates that MMSE is superior in accuracy because of the effect of a stable and a robust main lobe and adaptively suppressed side lobes. MMSE also provides precipitation measurements eliminating the directional dependence of a beam pattern for improving the accuracy of measurements. It is also shown that the estimated accuracies of the precipitation profiles are almost independent of the number of pulses. [ABSTRACT FROM PUBLISHER]

Quadratic forms play an important role in the development of several polarimetric and interferometric synthetic aperture radar (Pol-InSAR) methodologies, which are very powerful tools for earth observation. This paper investigates integrals of Pol-InSAR operators based on quadratic forms, with special interest on the Pol-InSAR coherence. A new operator, namely Trace Coherence, is introduced, which provides an approximation for the center of mass of the coherence region (CoRe). The latter is the locus of points on the polar plot containing all the possible coherence values. Such center of mass can be calculated as the integral of Pol-InSAR coherences over the scattering mechanisms (SMs). The trace coherence provides synthetic information regarding the partial target as one single entity. Therefore, it provides a representation, which is not dependent on the selection of one specific polarization channel. It may find application in change detection (e.g., coherent change detection and differential DEM), classification (e.g., building structure parameters), and modeling (e.g., for the retrieval of forest height). In calculating the integral of the Pol-InSAR coherences, an approximate trace coherence expression is derived and shown to improve the calculation speed by several orders of magnitude. The trace coherence approximation is investigated using Monte Carlo simulations and validated ESA (DLR) L-band quad-polarimetric data acquired during the AGRISAR 2006 campaign. The result of the analysis using simulated and real data is that the average error in approximating the integral of the coherence region is 0.025 in magnitude and 3° in phase (in scenarios with sufficiently high coherence). [ABSTRACT FROM PUBLISHER]

The context of space missions implies very strong constraints on the design of the instruments. For instance, mass, data rates, power consumption, clock frequency, and accuracy are very limited. These limitations degrade the performance and the accuracy of the measurements. This paper is related to the study of signals sampled at a low frequency. More exactly, we propose an oversampled pulse compression method for radar signals, which allows to reconstruction the signals with a much higher sampling rate. Consequently, this method allows significantly improving the multipath amplitude and the delay estimation accuracy. The efficiency of our method is clearly demonstrated with real measurements. These latter correspond to calibration measurements performed on ground with the radar COmets Nucleus Sounding Experiment by Radio-wave Transmission. The latter allows performing the tomography in the transmission of 67P Churyumov–Gerasimenko comets nucleus in the frame of the Rosetta/European Space Agency space mission. The results show that the proposed method allows estimating the delays with accuracy better than 10% of the sampling period in the case of a sampling frequency value close to the Nyquist limit. The analysis of the measured signals shows how a small frequency aliasing power degrades the delay estimation performance. This problem can be resolved using an estimation method based on an end-to-end signal modeling. [ABSTRACT FROM PUBLISHER]

Interferograms obtained by interferometric synthetic aperture radar generally include many singular points (SPs) originating from interference distortion and noise in the measurement. The filtering process is one of the key techniques in the generation of an accurate digital elevation model (DEM). In this paper, we propose a filter to remove SPs and related distortion using a complex-valued neural network in the spectral domain. It removes SPs nonlinearly and adaptively by referring to the statistics in neighboring windows in the 2-D frequency domain, where the textural features are represented in a more continuous manner than in the real space domain. Experiments demonstrate that the proposed method removes SPs and the distortion in SP-constructing four pixels, namely, the singular unit, more effectively than the conventional filters, resulting in the generation of a more accurate DEM. [ABSTRACT FROM AUTHOR]

Wind profilers (WPs) are a special category of ground-based radar instruments used to obtain 3-D profile of wind velocity along the altitude in atmosphere. They transmit radio wave pulses in very high frequency (VHF) or ultrahigh frequency (UHF) bands in vertical and near-vertical directions. The Doppler frequency shift and the time of flight of the echo signals from each beam are measured. These data are analyzed to get the radial velocities of atmospheric targets at all the ranges. The data must be processed from at least three noncoplanar beam directions together to estimate 3-D wind profile. The signal-to-noise ratio is very low for the higher range target echoes. Also, echoes are often contaminated by nonatmospheric signals like clutter, radio frequency interference, and so on. Determining accurate wind profile, especially at higher heights, becomes a challenging task due to these factors. This paper presents a novel algorithm for the estimation of radial velocity profile by processing the set of Doppler power spectra of all range bins. The algorithm identifies prospective atmospheric echoes components. Then it forms velocity profile trails connecting five range bins. The program rejects the profile trails that show velocity change higher than a predefined limit. The remaining trails are evaluated using a specially designed multiparameter cost function (MPCF). The trails with maximum cost are selected and then connected to construct the complete Doppler profile or the radial velocity profile. The key innovation in this method is an improvised function that is created by weighted addition of two terms. The first term is proportional to the signal power, and the second term is a nonlinear function of differential wind shear. This algorithm has been tried on multiple sets of the Indian mesosphere–stratosphere–troposphere radar data and the lower atmospheric WP radar data. The performance of this method is compared with other methods, namely, the adaptive moment estimation method and fuzzy logic approach. It has been observed that the new method shows excellent consistency in extracting the Doppler profile from the power spectral data. This method requires at least 30% lesser computations compared with other methods. The results of the MPCF method showed a very good match with the data obtained from concurrently operated Global Positioning System sonde, an independent wind profiling method. This paper also shows the results on the performance improvement using power spectra from the symmetrical beams. This algorithm shows a great promise as a tool for automatic Doppler profile tracing. [ABSTRACT FROM PUBLISHER]

Automatic extraction of road curbs from uneven, unorganized, noisy, and massive 3-D point clouds is a challenging task. Existing methods often project 3-D point clouds onto 2-D planes to extract curbs. However, the projection causes loss of 3-D information, which degrades the performance of the detection. This paper presents a robust, accurate, and efficient method to extract road curbs from 3-D mobile LiDAR point clouds. Our method consists of two steps: 1) extracting candidate points of curbs based on the proposed novel energy function and 2) refining candidate points using the proposed least cost path model. We evaluated the method on a large scale of residential area (16.7 GB, 300 million points) and an urban area (1.07 GB, 20 million points) mobile LiDAR point clouds. Results indicate that the proposed method is superior to the state-of-the-art methods in terms of robustness, accuracy, and efficiency. The proposed curb extraction method achieved a completeness of 78.62% and a correctness of 83.29%. Experiments demonstrate that our method is a promising solution to extract road curbs from mobile LiDAR point clouds. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for real-time automatic detection of nonforested and eroded areas in tropical rain forests. It is based on simple image algebra between color components, which enhances the contrast between brown and green areas. A successive segmentation through multiple thresholds, based on newly proposed indices of “brown color excess” and the “nonforest detection index,” leads to a binary map that clearly identifies forested and nonforested areas. Experimental tests, performed and compared with other recommended methods based on: region growing, active contours, and clustering, outperformed in detection accuracy ( Fm=96.4 %) and processing times ( Tp = 0.082 s). The method presented copes well with detecting regional irregularities and reduces frequent issues of nondetection, as well as false positives caused by intensity changes, shadows, and/or partial occlusions. The low processing times achieved with the proposed method allow real-time applications for low-cost unmanned aerial vehicle and unmanned aircraft systems with conventional camera equipment. [ABSTRACT FROM PUBLISHER]

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet-transformed domain via a relatively new spectral feature processing technique—singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracy over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artifacts introduced during the data acquisition process. By adding an extra spatial postprocessing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral–spatial classification methods. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce the concept, develop the theory, and demonstrate the advantages of fully focused coherent processing of pulse echoes from a nadir-looking pulse-limited radar altimeter. This process, similar to synthetic aperture radar (SAR) imaging systems, reduces the along-track resolution down to the theoretical limit equal to half the antenna length. We call this the fully focused SAR (FF-SAR) altimetry processing. The technique is directly applicable to SAR altimetry missions such as CryoSat-2, Sentinel-3, or Sentinel-6/Jason-CS. The footprint of an FF-SAR altimeter measurement is a narrow strip on the surface, which is pulse limited across track and SAR focused along track. Despite the asymmetry of the altimeter footprint, the fully focused technique may be useful for applications in which one needs to separate specific targets within highly heterogeneous scenes, such as in the case of sea ice lead detection, hydrology, and coastal altimetry applications. In addition, over rough homogeneous surfaces, such as the ocean or ice sheets, the improved multilooking capability of FF-SAR leads to a significant increase in the effective number of looks with respect to the delay/Doppler processing, resulting in better geophysical parameter estimation. The proposed processing technique was verified by processing full bit rate CryoSat-2 SAR mode data over the Svalbard transponder. Hydrology, sea ice, and open ocean applications are also demonstrated in this paper, showing the improvement of this technique with respect to conventional and delay/Doppler altimetry. [ABSTRACT FROM PUBLISHER]

Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and spectral resolutions. In this paper, we propose an integrated framework for the spatio–temporal–spectral fusion of remote sensing images. There are two main advantages of the proposed integrated fusion framework: it can accomplish different kinds of fusion tasks, such as multiview spatial fusion, spatio–spectral fusion, and spatio–temporal fusion, based on a single unified model, and it can achieve the integrated fusion of multisource observations to obtain high spatio–temporal–spectral resolution images, without limitations on the number of remote sensing sensors. The proposed integrated fusion framework was comprehensively tested and verified in a variety of image fusion experiments. In the experiments, a number of different remote sensing satellites were utilized, including IKONOS, the Enhanced Thematic Mapper Plus (ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Hyperspectral Digital Imagery Collection Experiment (HYDICE), and Système Pour l' Observation de la Terre-5 (SPOT-5). The experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Due to their low spatial resolution (≥ 10 km), passive microwave data from satellites often contain many mixed pixels. This is one of the main reasons that the retrieval accuracy of snow depth data is unsatisfactory for current practical demands. This paper proposes a multifrequency dual-polarized passive microwave unmixing method for snow. The land cover type of the observation area is partitioned into broadleaf forest, needleleaf forest, annual grass vegetation, and urban types. The component brightness temperature (CBT) of each land cover type, that is, unmixed data, with a 500-m data resolution, is obtained using the proposed unmixing method. Considering that the actual snow grain size was larger than 0.4 mm in the study area, Foster's snow depth retrieval algorithm, which refines the empirical coefficient, is employed in this paper. Compared with Chang's snow depth retrieval algorithm, the overall accuracy of snow depth data improved by approximately 29.2% using Foster's algorithm. Based on Foster's algorithm, the obtained results indicate that the overall accuracy of the snow depth data improved by approximately 22.9% when using the CBT compared with the original mixed-pixel method. The accuracy of snow depth data in annual grass vegetation is improved by approximately 38%, whereas those in needleleaf forest and broadleaf forest are improved by approximately 4.6% and 4.3%, respectively. The experimental results demonstrate that the CBT effectively improves the retrieval accuracy of snow depth data when compared with the case of using only the mixed-pixel method. [ABSTRACT FROM PUBLISHER]

The dielectric properties of vegetation reflect the coupling between the electromagnetic and physical properties of the material. Understanding dielectric behavior is essential for remote sensing applications. The traditional approach of measuring broadleaf permittivity involves exerting pressure on stacked leaves, which not only requires a complicated system to measure the pressure but also introduces a degree of uncertainty by changing the internal leaf microstructure. We propose a measurement technique that avoids the aforementioned shortcomings by using a vacuum packaging machine to remove the air from a sample bag, which is the first contribution of this paper. The proposed technique is validated against corn, cotton, and soybean leaf permittivity measurements. The second contribution of this paper involves how temperature is treated when modeling permittivity. The Debye–Cole dual-dispersion model, which is influential in vegetation remote sensing applications, is almost exclusively applied at 22 °C. However, it is assumed that this model can be extrapolated to other temperatures. Our analysis indicates that this model may overestimate permittivity at 28 °C, with mean relative errors (MREs) of up to 22% and 37% for relative permittivity and dielectric loss. Thus, we adopt the structure of the model due to its soundness and modify the model parameters. Overall, we used four different methods, among which the optimal method reduced the MRE by no more than −2.5%. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

The article offers information on periodical information for author regarding paper submission, Manuscript Page Format, and author names in the native language.

Frequency-modulation continuous-wave (FMCW) synthetic-aperture radar (SAR) can minimize the peak transmission power of sensors and reduce the size and weight of the systems. Wavenumber domain algorithm (WDA) is an accurate focusing method for SAR imaging. By using the exact signal form to compensate the phase error, WDA can achieve exact scene recovery from high-squint and long aperture data as long as the platform velocity is stable. In this paper, we introduce WDA to FMCW SAR and discuss the WDA-based FMCW SAR sparse imaging method. There are two main contributions of this paper: 1) a motion compensation-based WDA imaging method is introduced to correct the motion error that is often associated in practical airborne FMCW SAR data and 2) a novel WDA-based FMCW SAR sparse imaging method is developed to further improve the performance of recovered image. Compared with the typical WDA algorithm, the sparse imaging method can suppress the noise and sidelobes and perform the sparse scene recovery from the downsampled data. Experimental results via simulated and real airborne data verify the presented WDA-based FMCW SAR imaging methods. [ABSTRACT FROM AUTHOR]

Although the first filtering algorithms have been proposed more than 30 years ago, despeckling of synthetic aperture radar images is still an open issue. A new boost has been provided by nonlocal (NL) means filters. The idea of NL filters is to move from the exploitation of spatial neighboring pixels to the exploitation of similar pixels found across the image. The difference between the NL algorithms is mainly related to the definition of the similarity between pixels and how similar pixels are exploited in the restoration process. Generally, to define the similarity, the patches are adopted. In this paper, a new similarity criterion for selecting similar pixels is presented. It is based on the definition of the ratio patch between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. If the two pixels are similar, it is expected that the corresponding ratio patch will follow a specific statistical distribution. A modified version of the Kolmogorov–Smirnov distance is introduced to decide whether the statistical distribution of the ratio patch follows the expected one. To reduce the possible artifacts, anisotropy is exploited. Considering the proposed approach, the designed algorithm turns to be unbiased, able to provide the restored solution without any thresholding procedure, in which the tuning is substantially unsupervised and able to work with both single-look and multilook images. The algorithm has been tested on different simulated and real data. Qualitative and quantitative analyses validate the proposed approach, showing very good despeckling capabilities. [ABSTRACT FROM AUTHOR]

Achieving a proper time–frequency (TF) resolution is the key to extract information from seismic data using TF algorithms and characterize reservoir properties using decomposed frequency components. The generalized S-transform (GST) is one of the most widely used TF algorithms. However, it is difficult to choose an optimized parameter set for the whole seismic data set. In this paper, we propose to set the parameters of the GST adaptively using the instantaneous frequency (IF) of seismic traces. Our workflow begins with building a relationship between the parameter set of the GST and IF using a synthetic wedge model. We use the IF as an indicator for the time thickness of each trace in the wedge model. We then compute the TF spectrum of each trace using the GST with different parameter sets and compare the similarity between the computed TF spectrum and theory TF spectrum. The parameter set with the largest similarity is regarded as the best parameter set for each trace in the wedge model. In this manner, we build a relationship between the parameter set and IF value. We can finally choose the optimum parameter set for the GST according to the IF values of seismic traces. We name the proposed workflow as the self-adaptive GST (SAGST). To demonstrate the validity and effectiveness of the proposed SAGST, we apply it to synthetic seismic traces and field data. Both synthetic and real data examples illustrate that the SAGST can obtain a TF representation with a high TF resolution. [ABSTRACT FROM AUTHOR]