IEEE Transactions on Geoscience and Remote Sensing

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In this article, a theoretical study is carried out to seek an approach to remotely measure the thickness of the oil film. It is known that it is difficult to probe the oil thickness using a microwave radar due to the fact the dielectric constant of oil is very small compared with seawater for microwave frequencies. While in the range of terahertz (THz) electromagnetic (EM) wave frequency, the dielectric constants of oil and seawater are comparable, resulting in the THz EM fields scattered from the air–oil interface and those from the oil–water interface can produce constructive and destructive interference with the variation in the oil film thickness. Based on this principle, the sensitivity of the THz EM wave to the oil thickness is investigated in this work. First, the oil–seawater two-layer medium is equivalent to a single-layer medium by an equivalent dielectric constant model. Then, the THz EM scattering from the oil-covered sea surface is simulated using the first-order small slope approximation method (SSA-1) and the obtained equivalent dielectric constant. Meanwhile, the influences caused by the damping effect of the oil film, the reduction for friction velocity, and the change in equivalent permittivity on the normalized radar cross section (NRCS) are analyzed. The numerical results show that the NRCSs of THz frequencies are more sensitive to the change in oil thickness than that of microwave frequencies. This property makes the THz EM wave have the potential to probe the thickness of the oil film.

Deep learning has achieved great success in recent years, among which the convolutional neural network (CNN) method is outstanding in image segmentation and image recognition. It is also widely used in satellite imagery road extraction and, generally, can obtain accurate and extraction results. However, at present, the extraction of roads based on CNN still requires a lot of manual preparation work, and a large number of samples can be marked to achieve extraction, which has to take long drawing cycle and high production cost. In this article, a new CNN sample set production method is proposed, which uses the GPS trajectories of floating car as training set (GPSTasST), for the multilevel urban roads extraction from high-resolution remote sensing imagery. This method rasterizes the GPS trajectories of floating car into a raster map and uses the processed raster map to label the satellite image to obtain a road extraction sample set. CNN can extract roads from remote sensing imagery by learning the training set. The results show that the method achieves a harmonic mean of precision and recall higher than road extraction method from single data source while eliminating the manual labeling work, which shows the effectiveness of this work.

Modern lightning location systems (LLSs) remotely detect propagated electromagnetic fields and geolocate them for a better understanding of the lightning phenomenon. In recent years, they have become powerful tools for assessing situations where lightning may be the cause of damage. However, due to the random errors that occur in the remote sensing of electromagnetic propagation, such systems have median location accuracies between 50 and 100 m. This means that there is always some uncertainty in reported geolocations of lightning flashes. To date, there is no effective or standardized method for quantifying this uncertainty and using LLS reports as evidence. This article presents a unique solution to this problem by developing a Bayesian framework. In the field of forensic investigation and engineering, the Bayesian approaches to reporting evidence are widely accepted in legal forums. This article describes the necessary prior probability and likelihood functions (Gaussian mixture model, bivariate Gaussian, and Students’ t-distributions, respectively), and the framework is assessed by comparison with ground-truth events—photographed lightning events to a known location, the Brixton Tower in Johannesburg, South Africa. The framework has a true positive rate between 97% and 99% and 66% and 100% and a true negative rate between 98% and 99% using the bivariate Gaussian and Students’ t-likelihood functions, respectively. Utilizing the bivariate Students’ t-likelihood function achieves a much lower false-positive rate (0.05% to 0.2%) than the bivariate Gaussian likelihood function (0.7%–2%).

The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world’s oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance.

We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds.

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature ( $T_{b}$ ) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne $T_{b}text{s}$ , the inversion was also applied using the synthetic observations, where $T_{b}text{s}$ were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne $T_{b}text{s}$ are us- d, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne $T_{b}textrm {s}$ are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

With a growing number of different satellite sensors, data fusion offers great potential in many applications. In this work, a convolutional neural network (CNN) architecture is presented for fusing Sentinel-1 synthetic aperture radar (SAR) imagery and the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. The CNN is applied to the prediction of Arctic sea ice for marine navigation and as input to sea ice forecast models. This generic model is specifically well suited for fusing data sources where the ground resolutions of the sensors differ with orders of magnitude, here 35 km $times62$ km (for AMSR2, 6.9 GHz) compared with the 93 m $times87$ m (for sentinel-1 IW mode). In this work, two optimization approaches are compared using the categorical cross-entropy error function in the specific application of CNN training on sea ice charts. In the first approach, concentrations are thresholded to be encoded in a standard binary fashion, and in the second approach, concentrations are used as the target probability directly. The second method leads to a significant improvement in $R^{2}$ measured on the prediction of ice concentrations evaluated over the test set. The performance improves both in terms of robustness to noise and alignment with mean concentrations from ice analysts in the validation data, and an $R^{2}$ value of 0.89 is achieved over the independent test set. It can be concluded that CNNs are suitable for multisensor fusion even with sensors that differ in resolutions by large factors, such as in the case of Sentinel-1 SAR and AMSR2.

Based on the spatiotemporally weighted two-step method (STW-TSM), the spatiotemporal characteristics of the residual microwave brightness temperature (MBT) with the Mw7.9 Wenchuan earthquake on May 12, 2008 are revealed by satellite data from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor. Two significant MBT positive anomalies are found to be exactly in spatial accordance with two geological Quaternary zones, and the detailed geometric information of the MBT positive anomaly is found to be correlated with the microwave frequencies. After eliminating other possible influential factors, including surface temperature, vegetation index, land-surface roughness, and surface soil moisture under the conditions of space, time, and magnitude, and according to the microwave radiative transfer model, the dielectric variation in the ground surface is suggested to be the primary contributor of the MBT positive anomaly. The positive-hole (P-hole) theory is applied to interpret the geological preference of the MBT positive anomalies through a chain process: crustal stress enhancing—P-hole producing and flowing down stress gradients—surface P-hole accumulating—dielectric constant decreasing—and microwave radiation increasing. The stress-resulting effect of the dielectric decrease on MBT the increase provides a novel mechanism for microwave remote-sensing monitoring of crustal stress field alteration, earthquake preparation, and upcoming shocks. This research has a particular significance for searching potential georelations between the tectonic earthquake preparation and the abnormal satellite MBT.

Building change detection (CD), important for its application in urban monitoring, can be performed in near real time by comparing prechange and postchange very-high-spatial-resolution (VHR) synthetic-aperture-radar (SAR) images. However, multitemporal VHR SAR images are complex as they show high spatial correlation, prone to shadows, and show an inhomogeneous signature. Spatial context needs to be taken into account to effectively detect a change in such images. Recently, convolutional-neural-network (CNN)-based transfer learning techniques have shown strong performance for CD in VHR multispectral images. However, its direct use for SAR CD is impeded by the absence of labeled SAR data and, thus, pretrained networks. To overcome this, we exploit the availability of paired unlabeled SAR and optical images to train for the suboptimal task of transcoding SAR images into optical images using a cycle-consistent generative adversarial network (CycleGAN). The CycleGAN consists of two generator networks: one for transcoding SAR images into the optical image domain and the other for projecting optical images into the SAR image domain. After unsupervised training, the generator transcoding SAR images into optical ones is used as a bitemporal deep feature extractor to extract optical-like features from bitemporal SAR images. Thus, deep change vector analysis (DCVA) and fuzzy rules can be applied to identify changed buildings (new/destroyed). We validate our method on two data sets made up of pairs of bitemporal VHR SAR images on the city of L’Aquila (Italy) and Trento (Italy).

Supervised classification algorithms require a sufficiently large set of representative training samples to generate accurate land-cover maps. Collecting reference data is difficult, expensive, and unfeasible at the large scale. To solve this problem, this article introduces a novel approach that aims to extract reliable labeled data from existing thematic products. Although these products represent a potentially useful information source, their use is not straightforward. They are not completely reliable since they may present classification errors. They are typically aggregated at polygon level, where polygons do not necessarily correspond to homogeneous areas. Finally, usually, there is a semantic gap between map legends and remote sensing (RS) data. In this context, we propose an approach that aims to: 1) perform a domain understanding to detect the discrepancies between the thematic map domain and the RS data domain; 2) use RS data contemporary to the map to decompose the thematic product from the semantic and spatial viewpoints; and 3) extract a database of informative and reliable training samples. The database of weak labeled units is used for training an ensemble of classifiers on recent data whose results are then combined in a majority voting rule. Two sets of experimental results obtained on MS images by extracting training samples from a crop type map and the 2018 Corine Land Cover (CLC) map, respectively, confirm the effectiveness of the proposed approach.

Full waveform inversion (FWI) of ground-penetrating radar (GPR) data is becoming a promising technique to facilitate the interpretation of surface-GPR data and the mapping of the subsurface. However, more general FWIs still require a sufficient amount of RAM memory, and it is difficult to produce an accurate and representative reconstruction result due to a large amount of the Hessian matrix calculations and singular value decomposition (SVD). In this article, we developed a novel full-waveform approach of the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for surface-GPR data that is based on a quasi-Newton framework and the total variation (TV) regularization. The proposed approach uses an L-BFGS algorithm and combines a scale-transformation regularization technique to mitigate the ill-posed problem of inversion, which can impose a biparameter preinformation constraint to ensure the stability of inversion, and adaptive regularization weights are applied to improve the convergence efficiency of inversion. To demonstrate the novelty and effectiveness of the proposed scheme, we tested our FWI algorithm using synthetic data and in-site field data. In the testing, we focus on analyzing the influence of different aspects of the FWI results, including different scale factors, regularization weights, inversion strategies, acquisition configurations, initial models, and the noisy data set. In particular, the FWI experiment is performed to demonstrate the applicability of the proposed algorithm. The results show that the proposed algorithm can effectively reconstruct the biparameter near the subsurface with high accuracy, which makes our approach very attractive for attribute analysis applications and makes the surface-GPR FWI commercially viable.

FILtering by COHerence (FILCOH) is a novel technique for mitigating ground clutter echoes of weather radar, particularly those transmitting in multiple pulse repetition time (PRT) schemes. FILCOH takes advantage of the differences in correlation time for separating rain and ground echoes. In short, the longest time-lag coefficients of the autocorrelation function are used to estimate the ground clutter contribution which is then subtracted from the shortest lag coefficients before the extraction of the meteorological parameters using the conventional estimators. The initial results are obtained for uniform PRT data provided by the Degreane Horizon wind profiler radar. The analysis illustrates the behavior of FILCOH filtering and its high performance in such cases. The simulation results are then presented for 2- and 3-PRT pulse schemes using standard PRT ratios. For the 2-PRT scheme, FILCOH filters out the ground clutter echoes up to a clutter to signal ratio (CSR) of 65 dB for the received power and up to 45 and 60 dB for the radial velocity and spectrum width, respectively. For the 3-PRT scheme, ground clutter filtering is effective up to $CSR = 45$ dB for the received power and up to $CSR = 35$ dB for radial velocity and spectrum width. These results are confirmed by 3-PRT real data issued from the French X- and C-band radars. The observed performances are, however, a little less than those of the simulation since we observed a maximum 40-dB attenuation of the ground clutter echoes of the studied rain events.

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate Orthogonal Frequency-Division Multiplexing (OFDM) signals, or periodic WiFi beacon signals while in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, while a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In this article, we present experimental data which verifies our proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrate the capability for human activity sensing.

In this article, we propose a phase-derived velocity measurement (PDVM) method for high-speed spatial targets based on the stepped-frequency chirp signal (SFCS). This method is capable of accurately measuring the velocity of high-speed targets and yields root-mean-squared error values at the level of centimeters per second; therefore, it has great potential for measuring the micromotion of targets and is of significant importance for target recognition. The traditional phase-derived measurement method is not applicable for high-speed targets. The main challenge that we have solved is how to extract the echo phase from the high-resolution range profile, which is corrupted by range migration, intrapulse motion, and range straddling under high-speed target conditions. To guide the implementation of the proposed method in radar systems, constraint conditions for the compensation accuracy are thoroughly derived and systematically justified under different radar parameter settings. The simulation results are presented to validate the high accuracy of the method under various circumstances. In addition, the small-amplitude micromotion measurement capability of the proposed method is verified, and reconstruction of the target micromotion trajectory is demonstrated.

Many modern phased-array radars (PARs) are multichannel systems that include multiple receivers for data acquisition. Each channel provides a signal from a group of Transmit/Receive modules comprising a section of the antenna. Channels typically consist of a full receive path, often with an independent local oscillator (LO) clock source. Such arrangement provides for beamforming flexibility on receive which can be applied in a digital domain. Consequently, the channel-to-channel phase and magnitude alignment is critical to maximizing the performance of the digital beamforming process and the accuracy of resulting detections and measurements. Herein, a novel method to improve such alignment using weather returns and achieve the improvement in the polarimetric variable estimates is described.

With the advances in radar technology, through-the-wall radar imaging (TWRI) has become a viable sensing modality that can allow fire-and-rescue personnel, police, and military forces to detect, localize, and identify targets behind opaque obstacles. Many of the existing TWRI approaches detect either stationary or moving targets but not both of them simultaneously. In this article, a method is proposed to detect both stationary and moving targets from a sequence of radar signals. The proposed method decomposes the 3-D radar data, i.e., frequency, space, and time data into a low-rank tensor and two sets of sparse images. One set of images comprises the stationary targets, and the other set of images contains the moving targets. Wall clutter removal and target detection are formulated into an optimization problem regularized by tensor low-rank, joint sparsity, and total variation constraints. Then, an alternating direction technique is developed to reconstruct the sets of stationary and moving target images. Experiments using simulated and real radar signals are conducted. The experimental results illustrate the effectiveness of the proposed method to detect and separate the stationary and moving targets into a pair of sparse images.

Model-based incoherent polarimetric decomposition is a frequently used technique to analyze multilook data of polarimetric synthetic aperture radars (POLSARs). The purpose of this study is to analyze and compare different model-based incoherent polarimetric decomposition algorithms from the polarimetric information change aspect. For the input of a model-based incoherent polarimetric decomposition algorithm, polarimetric entropy was used to represent the polarimetric information of a coherency matrix. For the output of a model-based incoherent polarimetric decomposition algorithm, there are usually several decomposed components. To quantitatively represent their total polarimetric information, a new concept, generalized polarimetric entropy, was proposed which generalized the concept of polarimetric entropy based on the information entropy additivity of information theory. Generalized polarimetric entropy consists of two parts named as polarimetric power entropy and polarimetric residual entropy, respectively. Polarimetric power entropy describes the distribution status of the Span values of all decomposed components. Polarimetric residual entropy represents the residual randomness of all decomposed components. With the three new concepts, eight model-based incoherent polarimetric decomposition algorithms were compared and analyzed. Two real POLSAR images, respectively, derived by the E-SAR airborne system of Germany and the GF-3 satellite of China were used for the experiments. Experimental results had illustrated several useful conclusions.