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Ship detection is one of the most important applications of polarimetric Synthetic Aperture Radar (SAR) systems. Current ship detection methods are susceptible to side flap interference, making it difficult to extract the target shape correctly. In addition, when ships are exceedingly dense and have different scales, adjacent ships may be considered as a single target because of the influence of strong sidelobes, causing missed detections. To address the issues of sidelobe interference and multi-scale dense ship detection, a ship detection method based on the polarimetric SAR gradient and the complex Wishart classifier is proposed. First, the Likelihood Ratio Test (LRT) gradient is introduced into the log-ratio gradient framework to apply it to the polarimetric SAR data. Then, a Constant False Alarm Rate (CFAR) detector is applied to the gradient image to map the ship boundaries accurately. Second, the complex Wishart iterative classifier is used to detect the strong scattering part of the ship, which can eliminate most clutter interference and maintain the ship’s shape details. Finally, the LRT detection and complex Wishart classifier detection results are fused. Thus, not only the strong sidelobe interference can be greatly suppressed, but the dense targets with different scales are also distinguished and accurately located. This study performs comparative experiments on three polarimetric SAR images from the ALOS-2 satellite. Experimental results show that compared with the existing methods, the proposed algorithm has fewer false alarms and missed detections and can effectively overcome the problems of sidelobe interference while maintaining the shape details.

Data, in deep learning (DL), are crucial to detect ships in synthetic aperture radar (SAR) images. However, SAR image annotation limitations hinder DL-based SAR ship detection. A novel data-selection method and teacher–student model are proposed in this paper to effectively leverage sparse labeled data and improve SAR ship detection performance, based on the semi-supervised oriented object-detection (SOOD) framework. More specifically, we firstly propose a SAR data-scoring method based on fuzzy comprehensive evaluation (FCE), and discuss the relationship between the score distribution of labeled data and detection performance. A refined data selector (RDS) is then designed to adaptively obtain reasonable data for model training without any labeling information. Lastly, a Gaussian Wasserstein distance (GWD) and an orientation-angle deviation weighting (ODW) loss are introduced to mitigate the impact of strong scattering points on bounding box regression and dynamically adjusting the consistency of pseudo-label prediction pairs during the model training process, respectively. The experiments results on four open datasets have demonstrated that our proposed method can achieve better SAR ship detection performances on low-proportion labeled datasets, compared to some existing methods. Therefore, our proposed method can effectively and efficiently reduce the burden of SAR ship data labeling and improve detection capacities as much as possible.

With the rapid development of synthetic aperture radar (SAR) techniques, satellite systems’ capabilities to acquire information are continually improving. The PAZ satellite, with its high resolution and wide scanning swath, can provide high-quality data support for SAR applications. Oil tanks serve as energy storage devices, and their identification holds significant value in both military and civilian fields. Challenges in the detection and recognition of oil tanks using classical methods include poor detection, slow computation speed, and multiple windows of correct recognition. This paper centers on the analysis of oil tanks using PAZ data. We employ a sliding-window approach to acquire candidate target windows, process the windows through Weibull distribution modeling and hole filling, and extract target features using the monogenic signal based on regional L2 norm. The results demonstrate that the proposed method effectively improves the accuracy, and the model exhibits strong generalization ability and robustness.

Introduction: Traditional methods to estimate exposure to PM2.5 (particulate matter with less than 2.5 µm in diameter) have typically relied on limited regulatory monitors and do not consider human mobility and travel. However, the limited spatial coverage of regulatory monitors and the lack of consideration of mobility limit the ability to capture actual air pollution exposure.Methods: This study aims to improve traditional exposure assessment methods for PM2.5 by incorporating the measurements from a low-cost sensor network (PurpleAir) and regulatory monitors, an automated machine learning modeling framework, and big human mobility data. We develop a monthly-aggregated hourly land use regression (LUR) model based on automated machine learning (AutoML) and assess the model performance across eight metropolitan areas within the US.Results: Our results show that integrating low-cost sensor with regulatory monitor measurements generally improves the AutoML-LUR model accuracy and produces higher spatial variation in PM2.5 concentration maps compared to using regulatory monitor measurements alone. Feature importance analysis shows factors highly correlated with PM2.5 concentrations, including satellite aerosol optical depth, meteorological variables, vegetation, and land use. In addition, we incorporate human mobility data on exposure estimates regarding where people visit to identify spatiotemporal hotspots of places with higher risks of exposure, emphasizing the need to consider both visitor numbers and PM2.5 concentrations when developing exposure reduction strategies.Discussion: This research provides important insights for further public health studies on air pollution by comprehensively assessing the performance of AutoML-LUR models and incorporating human mobility into considering human exposure to air pollution.

ABSTRACTCompact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning.

Abstract The widespread COVID-19 pandemic fundamentally changed many people’s ways of life. With the necessity of social distancing and lock downs across the United States, evidence shows more people engage in outdoor activities. With the utilization of location-based service (LBS) data, we seek to explore how visitation patterns to national parks changed among communities of color during the COVID-19 pandemic. Our results show that visitation rates to national parks located closer than 347 km to individuals have increased amidst the pandemic, but the converse was demonstrated amongst parks located further than 347 km from individuals. More importantly, COVID-19 has adversely impacted visitation figures amongst non-white and Native American communities, with visitation volumes declining if these communities are situated further from national parks. Our results show disproportionately low-representations amongst national park visitors from these communities of color. African American communities display a particularly concerning trend whereby their visitation to national parks is substantially lower amongst communities closer to national parks.

Hydroxysafflor yellow A (HSYA) is derived from Carthamus tinctorius L. (Honghua in Chinese) and is used to treat cardiovascular and cerebrovascular disease. However, the mechanism by which HSYA treats ischemic stroke following atherosclerosis (ISFA) remains unclear. The targets and pathways of HSYA against ISFA were obtained using network analysis. A total of 3335 potential IFSA-related targets were predicted using the GenCards and Drugbank databases, and a total of 88 potential HSYA-related targets were predicted using the Swiss Target Prediction database. A total of 62 HSYA-related targets against IFSA were obtained. The network was composed of HSYA, 62 targets, and 20 pathways. The top 20 targets were constructed via the protein–protein interaction (PPI) network. Gene Ontology analysis revealed that the targets were involved in signal transduction, protein phosphorylation, the cytoplasm, the plasma membrane, the cytosol, zinc ion binding, ATP binding, protein kinase binding/activity, and enzyme binding. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the pathways were associated with cancer, inflammatory mediator regulation of the transient receptor potential channels, and microRNA in cancer. Additionally, molecular docking indicated that HSYA mainly interacts with five targets, namely interleukin 1 beta (IL-1β), signal transducer and activator of transcription 3 (STAT3), E1A-binding protein p300 (EP300), protein kinase C alpha (PRKCA), and inhibitor of nuclear factor kappa B kinase subunit beta (IKBKB). In animal experiments, HSYA administration ameliorated the infarct size, neurological deficit score, histopathological changes, carotid intima-media thickness (IMT), and blood lipid level (total cholesterol and triglycerides). Immunochemistry and quantitative PCR showed that HSYA intervention downregulated the expression of STAT3, EP300, PRKCA, and IKBKB, and the enzyme-linked immunoassay showed reduced IL-1β levels. The findings of this study provide a reference for the development of anti-ISFA drugs.

Forests cover approximately one-third of the Earth’s land surface and constitute the core region of the carbon cycle on Earth. The paramount importance and multi-purpose applications of forest monitoring have gained widespread recognition over recent decades. Polarimetric synthetic aperture radar interferometry (PolInSAR) has been demonstrated as a promising technique to retrieve the forest height over large areas with a limited cost. This paper presents an overview of forest height inversion (FHI) techniques based on PolInSAR data. Firstly, we introduce the basic theories of PolInSAR and FHI procedures. Next, we review the established data-based algorithms for single-baseline data and describe innovative techniques related to multi-baseline data. Then, the model-based algorithms are also introduced with their corresponding forest scattering models under multiple data acquisition modes. Subsequently, a case study is presented to demonstrate the applicable scenarios and advantages of different algorithms. Model-based algorithms can provide accurate results when the scene and forest properties are well understood and the model assumptions are valid. Data-based algorithms, on the other hand, can handle complex scattering scenarios and are generally more robust to uncertainties in the input parameters. Finally, the prospect of forest height inversion was analyzed. It is our hope that this review will provide guidelines to future researchers to enhance further FHI algorithmic developments.

Abstract Background Sterile inflammation is a key pathological process in stroke. Inflammasome activation has been implicated in various inflammatory diseases, including ischemic stroke and hemorrhagic stroke. Hence, targeting inflammasomes is a promising approach for the treatment of stroke. Methods We applied bibliometric methods and techniques. The Web of Science Core Collection was searched for studies indexed from database inception to November 26, 2020. We generated various visual maps to display publications, authors, sources, countries, and keywords. Results Our literature search yielded 427 publications related to inflammasomes involved in stroke, most of which consisted of original research articles and reviews. In particular, we found that there was a substantial increase in the number of relevant publications in 2018. Furthermore, most of the publications with the highest citation rates were published in 2014. Relatively, the field about inflammasomes in stroke developed rapidly in 2014 and 2018. Many institutions contributed to these publications, including those from China, the United States, and worldwide. We found that NLR family pyrin domain containing 3 (NLRP3) was the most studied, followed by NLRP1, NLRP2, and NLRC4 among the inflammasomes associated with stroke. Analysis of keywords suggested that the most studied mechanisms involved dysregulation of extracellular pH, efflux of Ca2+ ions, dysfunction of K+/Na+ ATPases, mitochondrial dysfunction, and damage to mitochondrial DNA. Conclusions Given the potential diagnostic and therapeutic implications, the specific mechanisms of inflammasomes contributing to stroke warrant further investigation. We used bibliometric methods to objectively present the global trend of inflammasomes in stroke, and to provide important information for relevant researchers.

As the base of Synthetic Aperture Radar (SAR) image processing, the registration of polarimetric SAR images requires high accuracy and a fast speed. Most methods used to register polarimetric SAR images based on deep learning are combined with patch matching and iterative estimation, e.g. the random sample consensus algorithm. However, end-to-end deep convolutional neural networks have not been used in the non-iterative affine registration of polarimetric SAR images. This paper proposes a framework for end-to-end polarimetric SAR image registration that is based on weakly-supervised learning and uses no image patch processing or iterative parameter estimation. First, feature extraction is performed on input image pairs to obtain dense feature maps with the most relevant k matches kept for each feature point. To filter the matched feature pairs, the 4D sparse feature matching maps are then fed into a 4D sparse convolutional network based on neighborhood consensus. Lastly, the affine parameters are solved by the weighted least square method according to the degree of confidence of the matches, which enables the affine registration of the input image pair. As test image pairs, we use farmland data from Wallerfing, Germany obtained by the RADARSAT-2 satellite and Zhoushan port data from China obtained by the PAZ satellite. Comprehensive experiments were conducted on polarimetric SAR image pairs using different orbit directions, imaging modes, polarization types and resolutions. Compared with four existing methods, the proposed method was found to have high accuracy and a fast speed.

The comprehensive utilization of images from various satellite sensors can significantly increase the performance of remote sensing applications and has, therefore, attracted extensive research attention. One of the essential challenges that research encounters comes from multisource image registration. This article proposes a novel region-based image registration method for multisource images. The proposed method exploits the region features of input images, which provide more consistent and common information of the multisource data. The image region features are extracted based on image semantic segmentation using the deep convolutional neural network approach. The final registration result is a pixel-level output corresponding to the input images. The proposed registration scheme overcomes the limits of traditional feature extraction methods (e.g., point feature) adopted in previous registration schemes. Results indicate that the proposed method has good performance for the multisource remote sensing image registration and can serve as a building block for the fusion of multisource images.

Abstract An adaptive quadrature method is proposed and implemented with the full‐wave MoM‐SMCG to accurately and efficiently solve the electromagnetic scattering from randomly rough surfaces. Without using high‐order basis functions and dense surface discretisation, high numerical accuracy of computing the inner integrals for near‐field impedance matrix elements is ensured by exploiting the orthogonal Legendre polynomials. Far‐field interactions are calculated using the acceleration algorithms and the iterative solver to archive reasonable computational complexity and memory consumption. The proposed method is validated on solving the electromagnetic scattering and emission from rough ocean surfaces with fine‐scale roughness and large dielectric constants. By comparing to the existing methods, simulation results indicate the proposed method has high computational efficiency and memory saving with also maintaining good accuracy.

Abstract Automatic registration of multi‐source remote sensing data is a challenging task due to the high non‐linearity of radiometric differences among various data. Feature extraction is a key enabling technique in registration algorithm design. In this letter, we prove the feasibility of using extracted regional features for multi‐source image registration. In particular, regional features can be extracted through pixel‐level image classification, and existing off‐the‐shelf algorithms can be directly adopted. Experiment results demonstrate the robustness of the regional‐feature‐based registration method.

Abstract This study proposes a new scattering vector parameterisation method for interpreting the coherent target scattering. This new scattering model, which has the same form as the α/β model, consists of five polarimetric parameters including the normalisation factor. Validation is performed on ALOS‐2/PALSAR data. In comparison with the current vector models, the new coherent scattering vector is superior in distinguishing targets with different physical scattering behaviours.

Mannitol, a representative of hyperosmolar therapy, is indispensable for the treatment of malignant cerebral infarction, but its therapeutic effect is limited by its exacerbation of blood-brain barrier (BBB) disruption. This study was to explore whether Danhong injection (DHI), a standardized product extracted from Salvia miltiorrhiza Bunge and Carthamus tinctorius L., inhibits the destructive effect of mannitol on BBB and thus enhancing the treatment of hemispheric ischemic stroke. SD rats were subjected to pMCAO followed by intravenous bolus injections of mannitol with/without DHI intervention. Neurological deficit score, brain edema, infarct volume at 24 h after MCAO and histopathology, microvascular ultrastructure, immunohistochemistry and immunofluorescence staining of endothelial cell junctions, energy metabolism in the ischemic penumbra were assessed. Intravenous mannitol after MCAO resulted in a decrease in 24 h mortality and cerebral edema, whereas no significant benefit on neurological deficits, infarct volume and microvascular ultrastructure. Moreover, mannitol led to the loss of endothelial integrity, manifested by the decreased expression of occludin, junctional adhesion molecule-1 (JAM-1) and zonula occluden-1 (ZO-1) and the discontinuity of occludin staining around the periphery of endothelial cells. Meanwhile, after mannitol treatment, energy-dependent vimentin and F-actin, ATP content, and ATP5D expression were down-regulated, while MMP2 and MMP9 expression increased in the ischemic penumbra. All the insults after mannitol treatment were attenuated by addition of intravenous DHI. The results suggest DHI as a potential remedy to attenuate mannitol-related BBB disruption, and the potential of DHI to upregulate energy metabolism and inhibit the activity of MMPs is likely attributable to its effects observed.

Deep learning methods have been widely studied for Polarimetric synthetic aperture radar (PolSAR) land cover classification. The scarcity of PolSAR labeled samples and the small receptive field of the model limit the performance of deep learning methods for land cover classification. In this paper, a vision Transformer (ViT)-based classification method is proposed. The ViT structure can extract features from the global range of images based on a self-attention block. The powerful feature representation capability of the model is equivalent to a flexible receptive field, which is suitable for PolSAR image classification at different resolutions. In addition, because of the lack of labeled data, the Mask Autoencoder method is used to pre-train the proposed model with unlabeled data. Experiments are carried out on the Flevoland dataset acquired by NASA/JPL AIRSAR and the Hainan dataset acquired by the Aerial Remote Sensing System of the Chinese Academy of Sciences. The experimental results on both datasets demonstrate the superiority of the proposed method.

Abstract The anisotropic diffusion filtering is a classical tool in image processing due to its simplicity and effectiveness. In this letter, we propose a new diffusion‐like filter based on the polarimetric synthetic aperture radar (PolSAR) ratio gradients. The PolSAR ratio gradients combine the information of different polarisation channels and describe the local directional changes in polarimetric characteristics. The proposed filter is then built by utilising the new gradients to derive the diffusion coefficients. The experimental results on synthetic data and real PolSAR images demonstrate the superiority and effectiveness of the new method.

Danhong injection (DHI) is a compound Chinese medicine widely used in China for treatment of ischemic cardio-cerebrovascular diseases. However, limited data are available regarding the protective effect of DHI on the ischemic penumbra in ischemic stroke. This study aimed to investigate the effect of intravenous DHI on neuronal injure in the ischemic penumbra after cerebral ischemia/reperfusion (CI/R), focusing especially on the involvement of intracellular energy metabolism coupling. Male Sprague-Dawley rats were subjected to right middle cerebral artery occlusion for 60 min followed by reperfusion with or without intravenous DHI (0.5, 1.0, or 2.0 mL/kg) once daily for 7 days. Post-treatment with DHI ameliorated neurological defects, diminished cerebral infarction, alleviated cerebral edema, improved microcirculatory perfusion after 7days of reperfusion, and inhibited apoptosis and enhanced neuronal survival in the ischemic penumbra. In addition, DHI significantly ameliorated oxidative stress, reduced DNA damage, and inhibited the activation of PARP1/AIF pathway, thereby restoring cytoplasmic glycolytic activity. Furthermore, this drug increased PDH activity by inhibiting the HIF1α/PDK1 signaling pathway, thus eliminating the inhibitory effect of CI/R on mitochondrial metabolism. The results of this study suggest that DHI can alleviate cerebral edema after CI/R and rescue the ischemic penumbra, and these protective effects are due to the regulation of intracellular energy metabolic coupling.

Ship target detection using radar has important applications in the military and civilian fields. As a decoy, the corner reflector (CR) can successfully deceive a radar by its strong radar cross-section (RCS) to protect a ship. In order to discriminate between a CR and ship, this paper proposes a discrimination method based on three-dimensional characteristics. First, we deduce the basic scattering of CR by the high-frequency electromagnetic theory, and propose a CR decomposition which can solve the problem that the Krogager decomposition has terrible errors in clutter. Then, we introduce the definition of the main scattering polarization and give the multi-dimensional characteristic of CR. Subsequently, we analyze the spatial-time characteristic of radar based on the three-dimensional proportional guidance. With the CR mean square error (MSE), a CR discrimination method is proposed based on the time-spatial-polarization (TSP) joint domains. Finally, the proposed method is analyzed and compared using the fully polarimetric data of Feko software, which can achieve 95% discrimination probability and 4.1% false alarm probability.

Because transmitting polarization can be an arbitrary elliptical wave, and theoretically, there are numerous possibilities of hybrid dual-pol modes, therefore, it is necessary to explore the feature recognition and classification ability of compact polarimetric (CP) parameters under different transmitting and receiving modes to different ground objects. In this paper, we first simulated, extracted, and analyzed the scattering intensity of two types of rice of six temporal CP synthetic aperture radar (SAR) data under three transmitting modes. Then, during different phenology stages, the optimal parameters for distinguishing transplanting hybrid rice (T–H) and direct-sown japonica rice (D–J) were acquired. Finally, a decision tree classification model was established based on the optimal parameters to carry out the fine classification of the two types of rice and to verify the results. The results showed that this strategy can obtain a high classification accuracy for the two types of rice with an overall classification accuracy of more than 95% and a kappa coefficient of more than 0.94. In addition, and importantly, we found that the CP parameters in the 1103 period (harvest stage) were the best CP parameters to distinguish the two types of rice, followed by the 0730 (seedling–elongation stage), 0612 (seedling stage), and 0916 (heading–flowering stage) periods.

In recent years, the compact polarimetric (CP) synthetic aperture radar (SAR) has become a hotspot of SAR Earth observation. Meanwhile, CP SAR provides both relatively rich polarization information and large swath-width for rice mapping. Fine classification of rice paddy plays an important role in growth monitoring, pest prevention and yield estimation of rice. In this study, the multi-temporal CP SAR data were firstly simulated by fully polarimetric RADARSAT-2 data, and 22 CP parameters from each of the six temporal CP SAR data were extracted. Then we built a rice height-sensitive index (RHSI). Furthermore, a decision tree (DT) method was established by using the optimal CP parameters based on RHSI. Finally, the classification results of rice paddy based on DT and support vector machine (SVM) methods were compared. Results showed that the RHSI-DT method could obtain better results, with an overall accuracy of 97.94% and a kappa coefficient of 0.973, which was 2% higher and 0.03 larger than those of the SVM method. Besides, we found that the surface scattering of m-χ decomposition (m-χ_s (0627)) and ΔShannon entropy intensity Hi (Hi (1015)-Hi (0627)) were highly effective parameters to distinguish paddies of transplanting hybrid rice (T-H) and direct-sown japonica rice (D-J).

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.

Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m).

Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and efficient method for polarimetric SAR (PolSAR) image classification. The Markov random field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this paper, we firstly introduce two ways to construct the Wishart mixture model and compare their performances using real PolSAR data. Then, the better mixture model and two other classical statistically distributions are combined with MRF to construct the MRF models. In order to improve the robustness of the models, the constant false alarm rate (CFAR)-based edge penalty term and an adaptive neighborhood system are embedded into the MRF energy functional. Classification is implemented in two schemes, i.e., pixel-based and region-based classifications. Finally, agriculture fields are used as the test scenario to evaluate the robustness and applicability of these algorithms.

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance.

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.

A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users' biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual-based on the density of tweets-and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users' location-related and temporal biases, promising to benefit human mobility and urban studies in general.

Gasification of coal or biomass with in-situ CO2 capture is an emerging technology aiming to address the problem of climate change. Development of a CO2 sorbent with desirable properties and understanding the behavior of such a material in carbonation/calcination reactions is an important part of developing the technology. In this paper, we report experimental results describing the carbonation behavior of three synthetic CaO-based sorbents. We also present a physically-based model of the reactive transport processes in sorbent particles, which have complicated pore structures. This modeling is based on the conditional approach (i.e., conditional moment closure (CMC)), which has proven to be successful in modeling reactive transport phenomena in porous media. The model predictions are in good agreement with the experimental data.

Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.

Developing mobile geo-information systems for sensor web applications involves technologies that can access linked geographical and semantically related Internet information. Additionally, in tomorrow’s Web 4.0 world, it is envisioned that trillions of inexpensive micro-sensors placed throughout the environment will also become available for discovery based on their unique geo-referenced IP address. Exploring these enormous volumes of disparate heterogeneous data on today’s location and orientation aware smartphones requires context-aware smart applications and services that can deal with “information overload”. 3DQ (Three Dimensional Query) is our novel mobile spatial interaction (MSI) prototype that acts as a next-generation base for human interaction within such geospatial sensor web environments/urban landscapes. It filters information using “Hidden Query Removal” functionality that intelligently refines the search space by calculating the geometry of a three dimensional visibility shape (Vista space) at a user’s current location. This 3D shape then becomes the query “window” in a spatial database for retrieving information on only those objects visible within a user’s actual 3D field-of-view. 3DQ reduces information overload and serves to heighten situation awareness on constrained commercial off-the-shelf devices by providing visibility space searching as a mobile web service. The effects of variations in mobile spatial search techniques in terms of query speed vs. accuracy are evaluated and presented in this paper.

On 10 August 2016 China launched the GF-3, its first C-band polarimetric synthetic aperture radar (SAR) satellite, which was put into operation at the end of January, 2017. GF-3 polarimetric SAR has many advantages such as high resolution and multi-polarization imaging capabilities. Polarimetric SAR can fully characterize the backscatter property of targets, and thus it is of great interest to explore the physical scattering mechanisms of terrain types, which is very important in interpreting polarimetric SAR imagery and for its further usages in Earth observations. In this paper, focusing on target scattering characterization and feature extraction, we generalize the Δ α B / α B method, which was proposed under the reflection symmetric assumption, for the general backscatter process to account for both the reflection symmetry and asymmetry cases. Then, we evaluate the performances of physical scattering mechanism analysis methods for GF-3 polarimetric SAR imagery. Radarsat-2 data acquired over the same area is used for cross validation. Results show that GF-3 polarimetric SAR data has great potential for target characterization, especially for ocean area observation.

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Understanding human mobility patterns is of great importance for urban planning, traffic management, and even marketing campaign. However, the capability of capturing detailed human movements with fine-grained spatial and temporal granularity is still limited. In this study, we extracted high-resolution mobility data from a collection of over 1.3 billion geo-located Twitter messages. Regarding the concerns of infringement on individual privacy, such as the mobile phone call records with restricted access, the dataset is collected from publicly accessible Twitter data streams. In this paper, we employed a visual-analytics approach to studying multi-scale spatiotemporal Twitter user mobility patterns in the contiguous United States during the year 2014. Our approach included a scalable visual-analytics framework to deliver efficiency and scalability in filtering large volume of geo-located tweets, modeling and extracting Twitter user movements, generating space-time user trajectories, and summarizing multi-scale spatiotemporal user mobility patterns. We performed a set of statistical analysis to understand Twitter user mobility patterns across multi-level spatial scales and temporal ranges. In particular, Twitter user mobility patterns measured by the displacements and radius of gyrations of individuals revealed multi-scale or multi-modal Twitter user mobility patterns. By further studying such mobility patterns in different temporal ranges, we identified both consistency and seasonal fluctuations regarding the distance decay effects in the corresponding mobility patterns. At the same time, our approach provides a geo-visualization unit with an interactive 3D virtual globe web mapping interface for exploratory geo-visual analytics of the multi-level spatiotemporal Twitter user movements.