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TanDEM-X interferometric synthetic aperture radar (InSAR) data have demonstrated promising advantages and potential in recent years for the inversion of forest height. InSAR coherence becomes the primary input feature when a precise digital terrain model is unavailable, but the relationship between InSAR coherence and forest height remains uncertain because of the complexity of forest scenes. In this article, a method for retrieving canopy height in Mediterranean forests, characterized by short and sparse trees, using a single-pass bistatic TanDEM-X InSAR dataset is proposed. To improve the accuracy of forest height inversion from the uncertain correlation between InSAR coherence and canopy height, we begin by using the established SINC model with two semiempirical parameters and then expand the single curve into a collection of three curves, forming the multi-SINC model. To determine the optimal relationship (curve) between TanDEM-X InSAR coherence and canopy height, the problem is shifted from parameter inversion to classification. To solve the problem, we used optical remote sensing data, a small amount of light detection and ranging (LiDAR) data, and TanDEM-X InSAR data in combination with machine learning for classification. As a proof-of-concept, we conducted forest height retrieval at two study sites in Spain with complex terrain and diverse forest types. The results were verified by comparing them with LiDAR product forest height, which demonstrated improved performance (RMSE = 2.49 m and 1.7 m) compared with the semiempirical SINC model (RMSE = 3.28 m and 2.36 m).

Abstract To address the limitations of computer vision-assisted table tennis ball detection, which heavily relies on vision acquisition equipment and exhibits slow processing speed, we propose a real-time calculation method for determining the landing point of table tennis balls. This novel approach is based on spatial domain information and reduces the dependency on vision acquisition equipment. This method incorporates several steps: employing dynamic color thresholding to determine the centroid coordinates of all objects in the video frames, utilizing target area thresholding and spatial Euclidean distance to eliminate interference balls and noise, optimizing the total number of video frames through keyframe extraction to reduce the number of operations for object recognition and landing point detection, and employing the four-frame difference slope method and polygonal area determination to detect the landing point and area of the target object, thereby obtaining precise coordinates and their corresponding areas. Experimental results on the above method on the Jetson Nano development board show that the dynamic color thresholding method achieves a detection speed of 45.3 fps. The keyframe extraction method correctly identifies the landing point frames with an accuracy rate exceeding 93.3%. In terms of drop point detection, the proposed method achieves 78.5% overall accuracy in detecting table tennis ball drop points while ensuring real-time detection. These experiments validate that the proposed method has the ability to detect table tennis ball drop points in real time and accurately in low frame rate vision acquisition devices and real environments.

Abstract The Hippo pathway is crucial to organ size control and its dysregulation contributes to tumorigenesis. The aberrant activation of YAP1 was identified in gallbladder cancer (GBC). However, the underlying mechanism and role in GBC remains unclear. The C terminal fragment of Mucin16, also known as carbohydrate antigen 125 (CA125) encoded product, MUC16c, plays extensive roles in tumor initiation and development. Our study showed that MUC16c binding with 14‐3‐3ε disrupted the interaction of 14‐3‐3ε and phosphorylated yes‐associated protein 1 (YAP1), which led to the activation of YAP1 in GBC. Furthermore, MUC16c decreased the phosphorylation of YAP1 at serine 397 (ser397) by inhibiting LATS1, which upregulated YAP1 protein stability. Interestingly, there was a potential Src kinase site in the MUC16c fragment. The MUC16c_del15Y polypeptides with the deletion of the Src kinase site promoted the interaction of YAP1 with 14‐3‐3ε and downregulated the YAP1 protein levels. Consistently, SU6656, a Src kinase inhibitor also blocked the activation of YAP1 by MUC16c. The MUC16c_del15Y polypeptides decreased GBC cell proliferation in vitro and the growth of xenograft tumors in vivo. Our study revealed the underlying mechanism of the activation of MUC16c on YAP1 mediated by Src signaling and the antitumor effect of MUC16c_del15Y, providing a potential target for GBC therapy.

Shanghai Pudong International Airport (PDIA), with its east side built along the coast with weak geological conditions, is prone to uneven foundation settlement due to the consolidation and compression of soil and erosion of coastal tides, affecting the safe operation of the airport. Therefore, it is crucial to conduct dynamic subsidence monitoring within the airport, especially in the runway area. 29 scenes of ascending track Sentinel-1A radar images from August 2016 to June 2018 are selected to perform surface deformation inversion based on PS-InSAR and improved SBAS-InSAR for PDIA and its around coastal area. Through cross-validation, the reliability of the time-series InSAR technique for dynamic monitoring of surface deformation of coastal zone infrastructures is confirmed. The results show severely uneven settlement. By combining the monitoring results with the local geological and hydrological dataset, the driving factors of differential deformation of the infrastructures are analyzed, including stratigraphic geological conditions, ground loadings, foundation treatment methods, water erosion, and groundwater level changes. Finally, the time-series deformation characteristics and the causes of PDIA's runway are emphasized based on the PS deformation results. This case provides a reference for the safety management of critical infrastructure in coastal areas using advanced InSAR technique.

Summary: Background: Ultrasonography is the most widely used technique to diagnose echinococcosis; however, challenges in using this technique and the demand on medical resources, especially in low-income or remote areas, can delay diagnosis. We aimed to develop a deep convolutional neural network (DCNN) model based on ultrasonography to identify echinococcosis and its types, especially alveolar echinococcosis. Methods: This retrospective, large-scale, multicentre study used ultrasound images from patients assessed at 84 hospitals in China, obtained between Jan 1, 2002, and Dec 31, 2021. Patients with a diagnosis of cystic echinococcosis, alveolar echinococcosis, or seven other types of focal liver lesions were included. We tested ResNet-50, ResNext-50, and VGG-16 as the backbone network architecture for a classification DCNN model and input the perinodular information from the ultrasound images. We trained and validated the DCNN model to diagnose and classify echinococcosis using still greyscale ultrasound images of focal liver lesions in four stages: differentiating between echinococcosis and other focal liver lesions (stage one); differentiating cystic echinococcosis, alveolar echinococcosis, and other focal liver lesions (stage two); differentiating cystic echinococcosis, alveolar echinococcosis, benign other focal liver lesions, and malignant focal liver lesions (stage three); and differentiating between active and transitional cystic echinococcosis and inactive cystic echinococcosis (stage four). We then tested the algorithm on internal, external, and prospective test datasets. The performance of DCNN was also compared with that of 12 radiologists recruited between Jan 15, 2022, and Jan 28, 2022, from Qinghai, Xinjiang, Anhui, Henan, Xizang, and Beijing, China, with different levels of diagnostic experience for echinococcosis and other focal liver lesions in a subset of ultrasound data that were randomly chosen from the prospective test dataset. The study is registered at ClinicalTrials.gov (NCT03871140). Findings: The study took place between Jan 1, 2002, and Dec 31, 2021. In total, to train and test the DCNN model, we used 9631 liver ultrasound images from 6784 patients (2819 [41·7%] female patients and 3943 [58·3%] male patients) from 87 Chinese hospitals. The DCNN model was trained with 6328 images, internally validated with 984 images, and tested with 2319 images. The ResNet-50 network architecture outperformed VGG-16 and ResNext-50 and was generalisable, with areas under the receiver operating characteristic curve (AUCs) of 0·982 (95% CI 0·960–0·994), 0·984 (0·972–0·992), and 0·913 (0·886–0·935) in distinguishing echinococcosis from other focal liver lesions; 0·986 (0·966–0·996), 0·962 (0·946–0·975), and 0·900 (0·872–0·924) in distinguishing alveolar echinococcosis from cystic echinococcosis and other focal liver lesions; and 0·974 (0·818–1·000), 0·956 (0·875–0·991), and 0·944 (0·844–0·988) in distinguishing active and transitional cystic echinococcosis from inactive echinococcosis in the three test datasets. Specifically, in patients with the hepatitis B or hepatitis C virus, the model could distinguish alveolar echinococcosis from hepatocellular carcinoma with an AUC of 0·892 (0·812–0·946). In identifying echinococcosis, the model showed significantly better performance compared with senior radiologists from a high-endemicity area (AUC 0·942 [0·904–0·967] vs 0·844 [0·820–0·866]; p=0·027) and improved the diagnostic ability of junior, attending, and senior radiologists before and after assistance with AI with comparison of AUCs of 0·743 (0·714–0·770) versus 0·850 (0·826–0·871); p

Abstract Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa’ Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.

Forest aboveground biomass (AGB) retrieval using synthetic aperture radar (SAR) backscatter has received extensive attention. The water cloud model (WCM), because of its simplicity and physical significance, has been one of the most commonly used models for estimating forest AGB using SAR backscatter. Nevertheless, forest AGB estimation using the WCM is usually based on simplified assumptions and empirical fitting, leading to results that tend to overestimate or underestimate. Moreover, the physical connection between the model and the polarimetric synthetic aperture radar (PolSAR) is not established, which leads to the limitation of the inversion scale. In this paper, based on the fully polarimetric SAR data from the Advanced Land Observing Satellite-2 (ALOS-2) Phased Array-type L-band Synthetic Aperture Radar (PALSAR-2), the relative contributions of the three major scattering mechanisms were first analyzed in a hilly area of southern China. On this basis, the traditional WCM was extended by considering the secondary scattering mechanism. Then, to establish the direct relationship between the vegetation scattering mechanism and forest AGB, a new relationship equation between the PolSAR decomposition model and the improved water cloud model (I-WCM) was constructed without the help of external data. Finally, a nonlinear iterative method was used to estimate the forest AGB. The results show that volume scattering is the dominant mechanism, accounting for more than 60%. Double-bounce scattering accounts for the smallest fraction, but still about 10%, which means that the contribution of the double-bounce scattering component is not negligible in forested areas because of the strong penetration capability of the long-wave SAR. The modified method provides a correlation coefficient R2 of 0.665 and a root mean square error (RMSE) of 21.902, which is an improvement of 36.42% compared to the traditional fitting method. Moreover, it enables the extraction of forest parameters at the pix scale using PolSAR data without the need for low-resolution external data and is thus helpful for high-resolution mapping of forest AGB.

Recently, many tough synthetic hydrogels have been created as promising candidates in fields such as smart electronic devices. In this paper, we propose a simple strategy to construct tough and robust hydrogels. Two-dimensional Ti3C2Tx MXene nanosheets and metal ions were introduced into poly(acrylamide-co-acrylic acid) hydrogels, the MXene nanosheets acted as multifunctional cross-linkers and effective stress-transfer centers, and physical cross-links were formed between Fe3+ and carboxylic acid. Under deformation, the coordination interactions exhibit reversible dissociation and reorganization properties, suggesting a novel mechanism of energy dissipation and stress redistribution. The design enabled the hydrogel to exhibit outstanding and balanced mechanical properties (tensile strength of up to 5.67 MPa and elongation at break of up to 508%). This study will facilitate the diverse applications of metallosupramolecular hydrogels.

The Slumgullion landslide is a large translational debris slide whose currently active part has likely been sliding for approximately 300 years. Its permanent motion and evolutionary processes have attracted the attention of many researchers. In order to study its mass wasting processes and evolution trend, the spatial–temporal displacement of the Slumgullion landslide was retrieved using an adaptive pixel offset tracking (POT) method with multi-track Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images. Based on three-dimensional displacement and slope information, we then revealed the spatial–temporal distribution of surface mass depletion or accumulation in the landslide, which provides a new perspective to analyze the evolutionary processes of landslides. The results indicate that the Slumgullion landslide had a spatially variable displacement, with a maximum displacement of 35 m. The novel findings of this study mainly include two parts. First, we found that the surface mass accumulated in the toe of the landslide and depleted in the top and middle area during the interval, which could increase the resisting force and decrease the driving force of the Slumgullion landslide. This result is compelling evidence which indicates the Slumgullion landslide should eventually tend to be stable. Second, we found that the distribution of geological structures can well explain some of the unique mass wasting in the Slumgullion landslide. The larger local mass depletion in the landslide neck area verifies that the sharp velocity increase in this region is not only caused by the reduction in width but is also significantly affected by the local normal faults. In summary, this study provides an insight into the relation between the landslide motion, mass volume change, and geological structure. The results demonstrate the great potential of multi-track airborne SAR for displacement monitoring and evolutionary analysis of landslides.

Interferometry and pixel offset tracking (POT) are two major technologies to measure ground deformation with repeat-pass synthetic aperture radar (SAR) observations. The SAR POT method can tolerate a much larger deformation gradient than interferometric SAR (InSAR), and thus it has been widely applied in monitoring earthquakes, volcanoes, landslides, glaciers, etc. However, there is an intrinsic trade-off between matching accuracy and detail preserving in terms of matching window size for the traditional SAR POT method using only single-polarization intensity. Taking advantage of polarimetric scattering properties, this study develops a novel POT method, named PCM-POT, towards mitigating the dependence of offset estimations on matching window size. It employs a generalized likelihood ratio test (GLRT) upon polarimetric covariance matrices (PCMs) to evaluate the similarity of two candidate pixels and estimate high-resolution surface displacements with high accuracy. The effectiveness and advantage of PCM-POT are demonstrated by an experiment of retrieving the displacements of the Slumgullion landslide from a pair of full-polarimetric UAVSAR images. Comparisons with the traditional POT indicate that using polarimetric scattering properties can reduce the uncertainty in offset estimations over heterogeneous sliding areas. Further experiments suggest that PCM-POT is also suitable for dual-polarimetric satellite SAR data.

In the time-series interferometric SAR (TSInSAR) technology, the phase linking (PL) algorithm can exploit all interferometric combinations to reconstruct the equivalent single master (ESM) interferograms for the phase quality improvement. According to the Cramer-Rao lower bound (CRLB) for PL, besides the number of spatial samples, the time-series coherence magnitude matrix determines the reconstruction performance of the ESM phases. With the abundance of time-series polarimetric SAR data, many polarimetric optimization algorithms for distributed scatterer (DS) have been introduced, mainly the averaged coherence magnitude maximization-based exhaustive search polarimetric optimization (ESPO) algorithm. However, traditional polarimetric optimization algorithms cannot work satisfactorily because of the unstable statistical characteristics. Similarly, the multi-temporal observations of each polarimetric channel contain interferometric information. From the perspective of redundant observation, introducing full-polarization information can increase the number of observations significantly. Therefore, based on the PL theory and the polarimetric optimization, this paper proposes to perform the joint diagonalization method to simultaneously extract the ESM interferometric phases of interest from three time-series coherence matrices in the Pauli basis polarizations, called the joint-polarization phase linking (JPPL) algorithm. In the experiments, the proposed JPPL algorithm can better improve the optimization performance of time-series interferometric phases of DSs than the HH and traditional ESPO algorithms in terms of speckle noise reduction and interferometric phase restoration.

During the Late Triassic, the southern margin of the Ordos Basin was influenced by the Indosinian Movement, in response to closure of the Paleo-Tethys Ocean. Thinly bedded tuff layers are exposed near the base of the Middle–Upper Triassic formations (Chang 8/7/6 members) in the Ordos Basin. Tuff samples from drilling core (well An-35) and outcrop (Motiangou section) samples in the Yanchang Formation were studied by the detailed field work. Igneous zircon crystals were picked and analyzed by the instruments of cathodoluminescence and laser ablation inductively coupled plasma mass spectrometery (LA-ICP-MS). The timing of volcanic activity was determined by means of high-precision zircon age. Combining tectonic setting and paleogeographic reconstruct, the ages of tuff layers indicate that volcanic activities occurred at three peaks (241.6 ± 1.1 Ma, 240.2 ± 1.1 Ma, and 233.5 ± 1.2 Ma) from the top of Chang 8 Member to the Chang 7 Member. The results implied that the volcanic activities lasted from Ladinian to Carnian, as well as define the sedimentary duration of the Chang 7 Member with a deposition rate of 1.34 cm/ka.

Phase unwrapping (PU) has always been a critical and challenging step in interferometric synthetic aperture radar (InSAR) data processing. Inspired by existing research, i.e., the PGNet, we propose a novel quality-guided 2-D InSAR PU method via deep learning, and regard PU as a two-stage process. In the first stage, the ambiguity gradient is estimated using the proposed global attention U-Net (GAUNet) architecture, which combines the classic U-Net structure and global attention mechanism. Then, in the second stage, the classical PU framework (e.g., the L1- or L2-norm) is applied as a post-processing operation to retrieve the absolute phase. Since class imbalance is a key factor affecting the estimation of ambiguity gradient, different strategies based on four commonly used quality maps are adopted to deal with the problem. The quality map is not only input as additional information for the guidance of the training process, but also participates in the construction of loss function. As a result, GAUNet can pay more attention to the nonzero ambiguity gradients. By using the number of residues as the evaluation metric, we can choose the optimum strategy for the restoration of the absolute phase. In addition to the simulated interferograms, the proposed method is tested both on a real topographic interferogram exhibiting rugged topography and phase aliasing and a differential interferogram measuring the deformation from MW 6.9 Hawaii earthquake, all yield state-of-art performance when comparing with the widely used traditional 2-D PU methods.

Existing volume scattering models play an important role in interpreting the scattering characteristics of vegetation canopies. A common assumption of these models is that a vegetation canopy consists of one type of particle. However, since a vegetation canopy comprises boughs, twigs, and leaves (or needles), they tend to form scatterers with various shapes, resulting in differing scattering processes. In this article, we propose a new volume scattering model. The proposed scattering model uses an isotropic plate, isotropic dihedral, and dipole particles to model vegetation scatterers. Different preferred orientation angles characterize the latter two types of particles. The proposed method is validated with AIRSAR and ALOS2/PALSAR2 datasets. The results show that the proposed model provides us an opportunity to extract refined scattering processes of vegetation canopies. In addition, it can be found that the proposed model shows potential in vegetation classification and biomass estimation, resulting from modeling a vegetation canopy as various types of particles.

BackgroundRecent studies have confirmed that AT-rich interactive domain-containing protein 1A (ARID1A) plays a critical role in tumorigenesis, but its role in gallbladder cancer (GBC) remains unclear.MethodsIn total, 224 patients from Zhongshan Hospital were recruited for this retrospective study. The clinicopathological and baseline characteristics of the patients were collected. Bioinformatics analysis was performed to reveal variations in genes and signaling pathways, and ARID1A and PD-L1 expression and the number of PD1+ tumor-infiltrating lymphocytes (TILs) were measured by immunohistochemical staining.ResultsARID1A expression was negatively correlated with overall survival in patients with GBC, and multivariate analysis identified ARID1A as an independent prognostic factor for overall survival. A heatmap and gene set enrichment analysis suggested that cytotoxic T lymphocyte signatures and immune-related signaling pathways were downregulated in ARID1A low tumors. Subsequent immunohistochemical staining confirmed that ARID1A expression was negatively correlated with PD-L1 expression and PD1+ TILs in the tumor microenvironment. The Kaplan–Meier analysis suggested that high ARID1A expression combined with low PD-L1 expression or low PD1+ TIL counts is associated with the best prognosis in patients with GBC.ConclusionARID1A inactivation can lead to a worse prognosis in patients with GBC, potentially by mediating immune evasion through the PD1/PD-L1 pathway.

Introduction Previous clinical studies have reported associations between the acromion index, lateral acromion angle, and critical shoulder angle and the occurrence of rotator cuff tears. The objective of this study was to analyze the correlations of these different anatomic parameters in geriatric Chinese Population. Methods Healthy geriatric Chinese participants (n = 66) and geriatric Chinese patients with rotator cuff tears (n = 70) identified between January 2019 and October 2020 were included in this study. Standardized true anteroposterior radiographs were used to measure the acromion index, lateral acromion angle, and critical shoulder angle in each study participant. Results The mean acromion index was significantly larger, the mean lateral acromion angle was significantly smaller, and the mean critical shoulder angle was significantly larger in geriatric patients with full-thickness rotator cuff tears compared with geriatric healthy participants. Conclusion There were a negative linear relationship between the acromion index and lateral acromion angle and a positive linear relationship between the acromion index and critical shoulder angle in geriatric patients with rotator cuff tear and geriatric healthy participants; we termed this phenomenon “Hypothesis of Acromion Index.” The acromion index, lateral acromion angle, and critical shoulder angle are independent predictors of rotator cuff tears in a geriatric Chinese population.

For time-series interferometric phases optimization of distributed scatterers (DSs), the SqueeSAR technology used the phase linking (PL) to extract the equivalent single-master (ESM) interferometric phases from the multilooking time-series coherence matrix. The Cramer–Rao lower bound (CRLB) for the PL describes the highest achievable estimation accuracy of the ESM phases, which depends on the number of looks and the time-series coherence magnitude matrix. With the abundance of time-series polarimetric SAR data, many scholars have studied the coherence magnitude-based polarimetric optimization methods for optimizing the DS’s time-series interferometric phases, for example, the widely-used exhaustive search polarimetric optimization (ESPO) algorithm. However, the traditional polarimetric optimization methods select the boundary extremums of the coherence region (CR) as the optimized complex coherence, which is usually biased from the free-noise one. Currently, in the polarimetric InSAR (PolInSAR) technology, Shen et al. innovatively considered polarimetric information as statistical samples and proposed the total power (TP) coherency matrix construction method for increasing the number of looks and reducing the interferometric phase noise. Therefore, to optimize the time-series interferometric phases for DS, this paper proposes performing a polarization stacking and extending the PolInSAR TP construction to the time-series PolInSAR (TSPolInSAR) data configuration, called the time-series TP (TSTP) method. Simulated and real experiments prove that the new TSTP construction method has better performance and higher efficiency than the single polarimetric and the traditional ESPO algorithms.

BackgroundCystic duct carcinoma (CDC) is a rare biliary malignancy with a low incidence and poor prognosis. However, the clinical landscape of the disease has not been clarified and no widely applicable classification system has been developed.MethodsSixty-two patients with CDC were included in this retrospective study, and a new classification system was established using imaging data. Blood indices, radiological characteristics, pathological features, surgical procedures, and overall survival data were collected. The efficacy of the new classification in predicting resectability was evaluated using receiver operating characteristic (ROC) curves, and K-means clustering and t-distributed stochastic neighbor embedding were applied to verify the conclusion.ResultsThe pT stage of patients with type II CDC was significantly worse than that of type I. Patients with type II CDC were more likely to experience distant metastasis and invasion of the nervous system, vascular system, and liver. The resectability of patients with type II CDC was significantly worse than that of patients with type I CDC. Patients with type II CDC had worse prognoses. ROC curve analysis and K-means clustering revealed that the new classification could better categorize patients with CDC than currently available systems.ConclusionPatients with type II CDC have significantly worse clinicopathological outcomes. The new classification system has better accuracy in grouping patients with CDC.

Abstract Background Gallbladder cancer (GC) is a malignant disease characterized with highly cellular heterogeneity and poor prognosis. Determining the intratumoral heterogeneity and microenvironment (TME) can provide novel therapeutic strategies for GC. Methods We performed the single‐cell RNA sequencing on the primary and lymph node metastatic gallbladder tumors and the adjacent normal tissues of five patients. The transcriptomic atlas and ligand–receptor‐based intercellular communication networks of the single cells were characterized. Results The transcriptomic landscape of 24,887 single cells was obtained and characterized as 10 cellular clusters, including epithelial, neuroendocrine tumor cells, T&NK cells, B cells, RGS5+ fibroblasts, POSTN+ fibroblasts, PDGFRA+ fibroblasts, endothelial, myeloid cells, and mast cells. Different types of GC harbored distinct epithelial tumor subpopulations, and squamous cell carcinoma could be differentiated from adenocarcinoma cells. Abundant immune cells infiltrated into adenocarcinoma and squamous cell carcinoma, rather than neuroendocrine neoplasms, which showed significant enrichment of stromal cells. CD4+/FOXP3+ T‐reg and CD4+/CXCL13+ T helper cells with higher exhausting biomarkers, as well as a dynamic lineage transition of tumor‐associated macrophages from CCL20hi/CD163lo, CCL20lo/CD163hi to APOE+, were identified in GC tissues, suggesting the immunosuppressive and tumor‐promoting status of immune cells in TME. Two distinct endothelial cells (KDR+ and ACKR1+), which were involved in angiogenesis and lymphangiogenesis, showed remarkable ligand–receptor interactions with primary GC cells and macrophages in gallbladder tumors. Conclusions This study reveals a widespread reprogramming across multiple cell populations in GC progression, dissects the cellular heterogeneity and interactions in gallbladder TME, and provides potential therapeutic targets for GC.

In polarimetric interferometric SAR (PolInSAR) technology, the random volume over ground (RVoG) model establishes the mapping relationship between polarimetric complex coherence and forest biophysical parameters (e.g., forest height). However, due to speckle noise and the finite multilooking effect, the real observed coherence region in the complex unitary circle (CUC) is an ellipse, which is biased by the ideal noise-free coherence region represented as a straight line by the RVoG model. Multilooking processing can reduce speckle noise at the cost of resolution loss. Therefore, this paper analyzes the influence of different multilooking sizes on forest height inversion. Experimental results show that the accuracy of forest height inversion first increases and then decreases with the increase in multilooking size, which means there exists an optimal size for PolInSAR forest estimation. From statistical analysis of the forest height estimation error, inversion accuracy mainly depends on estimation bias rather than estimation variance. This is mainly because, in a homogeneous forest area, a large multilooking size helps to reduce the statistical bias effect; in the textured area, the inversion accuracy benefits from a small multilooking size for avoiding the mixing of multiple types of ground targets.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was successfully launched. Due to its small spot size, multibeam configuration, high sampling rate, and strong immunity to terrain slopes, it has been regarded as a powerful tool for forest resources surveying and managing. However, the ICESat-2 photon cloud data contain considerable background photons, which discretely distribute in the background space of signal photons. Therefore, it is necessary to filter these noise photons. In this study, photons are divided into three categories: signal photons, noise photons far away from signal photons, and noise photons adjacent to signal photons. Based on the existing research, forward and backward elliptical distances were used to express the spatial relationship between two photons, and backward local density (BLD) was used to describe the density distribution of the photons. However, the single statistical parameter cannot clearly distinguish three types of photon cloud. Therefore, forward local density (FLD) and neighboring forward local density difference (NFLDD) also were defined to describe the density distribution of the photons. Finally, by combining the support vector machine (SVM), the above three density parameters were used to classify the photons by signal and noise photons. The proposed method was validated with photon cloud data acquired by the Simulated Advanced Terrain Laser Altimeter System (MATLAS), the Multiple Altimeter Beam Experimental Lidar (MABEL), and the ICESat-2 systems over different forested areas. The results demonstrated that the proposed method can well remove the noise photons and retain the signal photons without depending on any statistical assumptions or thresholds. The comprehensive accuracy of the three test sites was 0.99, 0.98, and 0.99, respectively, which was higher than those of the existing method. In addition, the total errors corresponding to the three test sites were about 0.4%, 0.5%, and 1.0% respectively, which were lower than those of the existing method.

Background/Aims: This study aimed at investigating the effects of metformin on the growth and metastasis of esophageal squamous cell carcinoma (ESCC) in vitro and in vivo. Methods: Two human ESCC cell lines EC9706 and Eca109 were selected and challenged with metformin in this study. Western blot assay was performed to detect th level of Bcl-2, Bax and Caspase-3. Scratch wound assay, transwell assay and Millicell invasion assay were used to assay the invasion and migration of EC9706 and Eca109 cells. Nude mice tumor models were used to assay the growth and lung metastasis of ESCC cells after metformin treatment. The plasma glucose level was also assayed. Results: We found that metformin significantly inhibited proliferation and induced apoptosis of both ESCC cell lines in a dose- and time-dependent manner, and the expression of Bcl-2 was down-regulated and Bax and Caspase-3 were up-regulated. Metformin significantly inhibited the invasion and migration of EC9706 and Eca109 cells (p < 0.05). mRNA and protein levels of MMP-2 and MMP-9 decreased significantly upon treatment with metformin of 10mM for 12, 24 and 48h in a time-dependent manner (p < 0.05). In line with in vitro results, in vivo experiments demonstrated that metformin inhibited tumorigenicity, inhibited lung metastasis and down-regulated the expression of MMP-2 and MMP-9. Moreover, we showed that metformin treatment did not cause significant alteration in liver and renal functions and plasma glucose level. Conclusion: Our study for the first time demonstrated the anti-invasive and anti-metastatic effects of metformin on human ESCC cells both in vitro and in vivo, which might be associated with the down-regulation of MMP-2 and MMP-9. As a whole, our results indicate the potential of metformin to be developed as a chemotherapeutic agent for patients with ESCC and might stimulate future studies on this area.

Within the context of large-scale symmetry, a study on deep vision servo multi-vision tracking coordination planning for sorting robots was conducted according to the problems of low recognition sorting accuracy and efficiency in existing sorting robots. In this paper, a kinematic model of a mobile picking manipulator was built. Then, the kinematics design of the orwX, Y, Z three-dimensional space manipulator was carried out, and a method of deriving and calculating the base position coordinates through the target point coordinates, the current moving chassis center coordinates and the determined manipulator grasping attitude conditions was proposed, which realizes the adjustment of the position and attitude of the moving chassis as small as possible. The multi-vision tracking coordinated sorting accounts 79.8% of the whole cycle. The design of a picking robot proposed in this paper can greatly improve the coordination symmetry of logistic package target recognition, detection and picking.

The key point of forest height and underlying topography inversion using synthetic aperture radar tomography (TomoSAR) depends on the accurate positioning of the phase centers of different scattering mechanisms. The traditional nonparametric spectrum analysis methods (such as beamforming and Capon) have limited vertical resolution and cannot accurately distinguish closely spaced scatterers. In addition, it is very difficult to accurately estimate the ground or canopy heights with single polarimetric SAR images because there is no guarantee that the vertical profile will generate two clear and separate peaks for all resolution cells. A polarimetric TomoSAR method based on SKP (sum of Kronecker products) decomposition and iterative maximum likelihood estimation is proposed in this paper. On the one hand, the iterative maximum likelihood TomoSAR method has a higher vertical resolution than that of the traditional methods. On the other hand, the separation of the canopy scattering mechanism and the ground scattering mechanism is conducive to the positioning of the phase centers. This method was applied to the inversion of forest height and underlying topography in a tropical forest over the TropiSAR2009 test site in Paracou, French Guiana with six passes of polarimetric SAR images. The inversion accuracy of underlying topography of the proposed method was up to 1.489 m and the inversion accuracy of forest height was up to 1.765 m. Compared with the traditional polarimetric beamforming and polarimetric capon methods, the proposed method greatly improved the inversion accuracy of forest height and underlying topography.

This paper develops a framework for extracting sub-canopy topography from the TanDEM-X digital elevation model (DEM) by fusing ALOS-2 PARSAR-2 interferometric synthetic aperture radar (InSAR) coherence and Global Ecosystem Dynamics Investigation (GEDI) data. The main idea of this method is to estimate the forest height signals caused by the limited penetration of the X-band into the canopy from the TanDEM-X DEM. To achieve this goal, a spaceborne repeat-pass InSAR coherent scattering model is first used to estimate the forest height by the ALOS-2 PARSAR-2 InSAR coherence (APIC), taking the GEDI canopy height as the reference. Then, a linear regression model of the TanDEM-X DEM Vegetation Bias (TDVB) depending on the forest height and the fraction of vegetation cover (FVC) is established and used to estimate the sub-canopy topography. The proposed method was validated by the data of the Amazon rainforest and a boreal forest in Canada. The results showed that the proposed method extracted the sub-canopy topography at the study sites in the tropical forest and boreal forest with the root mean square error of 4.0 m and 6.33 m, respectively, and improved the TanDEM-X DEM accuracy by 75.7% and 39.7%, respectively.

As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) not only indicates the speckle noise level in multi-look SAR data but also can be used for evaluating the region homogeneity level. Currently, time-series polarimetric (interferometric) SAR (TSPol(In)SAR) data are increasingly abundant, but traditional equivalent number of looks (ENL) estimators only use polarimetric information from a mono-temporal observation and do not consider the temporal characteristics or interferometric coherence of ground targets. Therefore, this paper puts forward four novel ENL estimators to overcome the restrictions of inadequate observation information. Firstly, based on the traditional trace moment estimator for polarimetric SAR data (TM-PolSAR), we extend it to both PolInSAR and TSPolInSAR data and then propose both TM-PolInSAR and TM-TSPolInSAR estimators, respectively. Secondly, for both TSPolSAR and single-reference TSPolInSAR data, we estimate the ENL by stacking the trace moments (STM) of multitemporal coherency matrices, called STM-TSPolSAR and STM-TSPolInSAR estimators, respectively. Therefore, these proposed ENL estimators can effectively deal with most of the requirements of TSPol(In)SAR data types in practical applications, mainly including statistical distribution modeling and region homogeneity evaluation. The simulation and real experiments detailedly compare the proposed four ENL estimators to the classical TM-PolSAR estimator and quantitatively analyze the estimation performance. The proposed estimators have obtained the ENL with less bias and standard deviation than the traditional estimator, especially in case of small spatial samples coherency matrices. Additionally, these STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators have provided more effective statistical characteristics with the increase of the time-series size. It has been demonstrated that the proposed STM-TSPolSAR estimator considers the time-varying polarimetric characteristics of the crop and detects many edges that the traditional estimator cannot discover, which means a superior capability of region homogeneity evaluation.

In the traditional single polarimetric persistent scatterers interferometric (PSI) technology, the amplitude dispersion index (ADI) is usually used to select persistent scatterer candidates (PSC). Obviously, based on single polarimetric information, it is difficult to use the statistical characteristics for comprehensively describing the temporal stability of scatterers, which leads to a decrease in persistent scatterer (PS) density. Considering that the temporal polarimetric stationarity of PS, the paper is based on complex Wishart distribution and proposes the polarimetric stationarity omnibus test (PSOT) for identifying PSC. The nonstationary pixels can be removed by the preset significance threshold, which reduces the subsequent processing error and the calculation cost. Then, the exhaustive search polarimetric optimization (ESPO) method is selected for improving the phase quality of PSCs while suppressing the sidelobe of the strong scatterer effectively. For validating the effectiveness of the proposed method, we select a time-series quad-polarimetric ALOS PALSAR-1 images in an urban area as experimental data and mainly perform five group experiments for detailed analysis, including the PSOT+ESPO, ADI+ESPO, ADI+HH, ADI+HV, and ADI+VV. The results show that the proposed PSOT+ESPO method has a better performance on both PSC selection and interferometric phase optimization aspects than that of other methods. Specifically, compared to the last four methods, both the PSCs and PSs identified by the proposed PSOT+ESPO are more concentrated in the high-coherence region. The PSs with the standard deviation (STD) less than 5mm in the PSOT+ESPO method account for 94% of all PSs, which is greater than that of the ADI+ESPO, ADI+HH, ADI+HV, and ADI+VV methods, respectively.

Background Rheumatoid arthritis (RA) is a chronic auto-inflammatory disorder of joints. The present study aimed to identify the key genes in RA for better understanding the underlying mechanisms of RA. Methods The integrated analysis of expression profiling was conducted to identify differentially expressed genes (DEGs) in RA. Moreover, functional annotation, protein–protein interaction (PPI) network and transcription factor (TF) regulatory network construction were applied for exploring the potential biological roles of DEGs in RA. In addition, the expression level of identified candidate DEGs was preliminarily detected in peripheral blood cells of RA patients in the GSE17755 dataset. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression levels of identified DEGs in RA. Results A total of 378 DEGs, including 202 up- and 176 down-regulated genes, were identified in synovial tissues of RA patients compared with healthy controls. DEGs were significantly enriched in axon guidance, RNA transport and MAPK signaling pathway. RBFOX2, LCK and SERBP1 were the hub proteins in the PPI network. In the TF-target gene network, RBFOX2, POU6F1, WIPF1 and PFKFB3 had the high connectivity with TFs. The expression status of 11 candidate DEGs was detected in GSE17755, the expression levels of MAT2A and NSA2 were significantly down-regulated and CD47 had the up-regulated tendency in peripheral blood cells of patients with RA compared with healthy individuals. qRT-PCR results of MAT2A, NSA2, CD47 were compatible with our bioinformatics analyses. Discussion Our study might provide valuable information for exploring the pathogenesis mechanism of RA and identifying the potential biomarkers for RA diagnosis.

Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas. SPICE is a sparse spectral estimation method that achieves a high vertical resolution, and takes account of the noise adaptively for each resolution cell. Thus, it does not require the user to select a hyperparameter. Furthermore, the used sparse basis not only ensures the sparsity of the forest canopy scattering contribution, but it can also keep the original sparse information of the ground contribution. The proposed method was tested in simulated experiments and the results demonstrated that W&O-SPICE can successfully reconstruct the vertical structure of a forest. Moreover, three P-band fully polarimetric airborne SAR images with non-uniformly distributed baselines were applied to reconstruct the vertical structure of a tropical forest in Mabounie, Gabon. The underlying topography and forest height were estimated, and the root-mean-square errors (RMSEs) were 6.40 m and 4.50 m with respect to the LiDAR digital terrain model (DTM) and canopy height model (CHM), respectively. In addition, W&O-SPICE showed a better performance than W&O-CS, beamforming, Capon, and the iterative adaptive approach (IAA).

Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R2 value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m3/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m3/ha, and the mean value was 135.759 m3/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.

Shale has been considered as good gas reservoir due to its abundant interior nanoscale pores. Thus, the study of the pore structure of shale is of great significance for the evaluation and development of shale oil and gas. To date, the most widely used approaches for studying the shale pore structure include image analysis, radiation and fluid invasion methods. The detailed pore structures can be studied intuitively by image analysis and radiation methods, but the results obtained are quite sensitive to sample preparation, equipment performance and experimental operation. In contrast, the fluid invasion method can be used to obtain information on pore size distribution and pore structure, but the relative simple parameters derived cannot be used to evaluate the pore structure of shale comprehensively and quantitatively. To characterize the nanoscale pore structure of shale reservoir more effectively and expand the current research techniques, we proposed a new method based on gas adsorption experimental data and the method of moments to describe the pore structure parameters of shale reservoir. Combined with the geological mixture empirical distribution and the method of moments estimation principle, the new method calculates the characteristic parameters of shale, including the mean pore size (mean), standard deviation (σ), skewness (Sk) and variation coefficient (c). These values are found by reconstructing the grouping intervals of observation values and optimizing algorithms for eigenvalues. This approach assures a more effective description of the characteristics of nanoscale pore structures. Finally, the new method has been applied to analyze the Yanchang shale in the Ordos Basin (China) and Longmaxi shale from the Sichuan Basin (China). The results obtained well reveal the pore characteristics of shale, indicating the feasibility of this new method in the study of the pore structure of shale reservoir.

With the increasing of satellite sensors, more available multi-source data can be used for large-scale high-precision crop classification. Both polarimetric synthetic aperture radar (PolSAR) and multi-spectral optical data have been widely used for classification. However, it is difficult to combine the covariance matrix of PolSAR data with the spectral bands of optical data. Using Hoekman’s method, this study solves the above problems by transforming the covariance matrix to an intensity vector that includes multiple intensity values on different polarization basis. In order to reduce the features redundancy, the principal component analysis (PCA) algorithm is adopted to select some useful polarimetric and optical features. In this study, the PolSAR data acquired by satellite Gaofen-3 (GF-3) on 19 July 2017 and the optical data acquired by Sentinel-2A on 17 July 2017 over the Dongting lake basin are selected for the validation experiment. The results show that the full feature integration method proposed in this study achieves an overall classification accuracy of 85.27%, higher than that of the single dataset method or some other feature integration modes.

Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.

The traditional nonlocal filters for polarimetric synthetic aperture radar (PolSAR) images are based on square patches matching to obtain homogeneous pixels in a large search window. However, it is still difficult for the regular patches to work well in the complex textured areas, even when the patch size has a small enough setting (e.g., 3 × 3 windows). Therefore, this paper proposes an adaptive nonlocal mean filter with shape-adaptive patches matching (ANLM) for PolSAR images. Mainly, the shape-adaptive (SA) matching patches are constructed by combining the polarimetric likelihood ratio test for coherency matrices (PolLRT-CM) and the region growing (RG), which is called PolLRT-CMRG. It is used to distinguish the homogeneous and heterogeneous pixels in textured areas effectively. Then, to enhance the filtering effect, it is necessary to take the adaptive threshold selection of similarity test (Simi-Test) into consideration. The simulated, low spatial resolution SAR580-Convair and high spatial resolution ESAR PolSAR image datasets are selected for experiments. We make a detailed quantitative and qualitative analysis for the filtered results. The experimental results have demonstrated that the proposed ANLM filter has better performance in speckle suppression and detail preservation than that of the traditional local and nonlocal filters.

Synthetic aperture radar tomography (TomoSAR) is a useful tool for retrieving the three-dimensional structure of buildings in urban areas, especially for datasets with a high spatial resolution. However, among the previous TomoSAR estimators, some cannot retrieve the 3-D structure of objects with a high elevation resolution, some cannot maintain the spatial resolution, and some require the selection of a hyperparameter. To overcome these limitations, this paper proposes a new nonparametric iterative adaptive approach with a model selection tool based on the Bayesian information criterion (IAA-BIC) for the application of TomoSAR in urban areas. IAA-BIC employs weighted least squares to acquire a high elevation resolution and works well for both distributed and coherent scatterers, even with single-look. Concurrently, IAA-BIC does not require the user to make any difficult selection regarding a hyperparameter. The proposed IAA-BIC estimator was tested in simulated experiments, and the results confirmed the advantages of the IAA-BIC estimator. Moreover, the three-dimensional structure of the Hubei Science and Technology Venture building in Wuhan, China, was retrieved through the IAA-BIC method with nine very high spatial resolution TerraSAR-X images. The height estimation accuracy for this building was about 1% and 4% relative to its real height for single-look and multi-look, respectively. In addition, a comparison between the IAA-BIC estimator and some of the typical existing TomoSAR estimators (Capon, MUSIC, and compressed sensing (CS)) was also carried out. The results indicate that the IAA-BIC estimator obtains a better resolution for coherent sources than Capon and MUSIC; notably, the IAA-BIC estimator obtains a similar performance to CS, but in less computation time.

Forest above ground biomass (AGB) extraction using Synthetic Aperture Radar (SAR) images has been widely used in global carbon cycle research. Classical AGB inversion methods using SAR images are mainly based on backscattering coefficients. The polarization coherence tomography (PCT) technology which can generate vertical profiles of forest relative reflectivity, has the potential to improve the accuracy of biomass inversion. The relationship between vertical profiles and forest AGB is modeled by some parameters defined based on geometric characteristics of the relative reflectivity distribution curve. But these parameters are defined without physical characteristics. Among these parameters, tomographic height (TomoH) is considered as the most important one. However, TomoH only corresponds to the highest volume relative reflectivity, which is lower than the actual forest height, affecting the accuracy of forest height and AGB inversion. In this paper, we introduce a new parameter, the canopy height (Hac), for AGB inversion by analyzing the vertical backscatter power loss. Then, we construct an inversion model based on the combination of the new parameter (Hac) and other parameters from the tomographic profile. The P-band polarimetric SAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign acquired over Krycklan Catchment are selected for the verification experiment at two different flight directions. The results show that Hac performs better in estimating forest height and AGB than TomoH does. The inversion root mean square error (RMSE) of the proposed method is 18.325 t ha−1, and the result of using TomoH is 21.126 t ha−1.

This paper describes an improved Constant False Alarm Rate (CFAR) ship detection algorithm in spaceborne synthetic aperture radar (SAR) image based on Alphastable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution.

With the development of high-resolution Synthetic Aperture Radar (SAR) systems, researchers are increasingly paying attention to the application of SAR offset tracking methods in ground deformation estimation. The traditional normalized cross correlation (NCC) tracking method is based on regular matching windows. For areas with different moving characteristics, especially the landslide boundary areas, the NCC method will produce incorrect results. This is because in landslide boundary areas, the pixels of the regular matching window include two or more types of moving characteristics: some pixels with large displacement, and others with small or no displacement. These two kinds of pixels are uncorrelated, which result in inaccurate estimations. This paper proposes a new offset tracking method with SAR images based on the adaptive matching window to improve the accuracy of landslide displacement estimation. The proposed method generates an adaptive matching window that only contains pixels with similar moving characteristics. Three SAR images acquired by the Jet Propulsion Laboratory’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system are selected to estimate the surface deformation of the Slumgullion landslide located in the southwestern Colorado, USA. The results show that the proposed method has higher accuracy than the traditional NCC method, especially in landslide boundary areas. Furthermore, it can obtain more detailed displacement information in landslide boundary areas.

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°.

Recently, a general polarimetric model-based decomposition framework was proposed by Chen et al., which addresses several well-known limitations in previous decomposition methods and implements a simultaneous full-parameter inversion by using complete polarimetric information. However, it only employs four typical models to characterize the volume scattering component, which limits the parameter inversion performance. To overcome this issue, this paper presents two general polarimetric model-based decomposition methods by incorporating the generalized volume scattering model (GVSM) or simplified adaptive volume scattering model, (SAVSM) proposed by Antropov et al. and Huang et al., respectively, into the general decomposition framework proposed by Chen et al. By doing so, the final volume coherency matrix structure is selected from a wide range of volume scattering models within a continuous interval according to the data itself without adding unknowns. Moreover, the new approaches rely on one nonlinear optimization stage instead of four as in the previous method proposed by Chen et al. In addition, the parameter inversion procedure adopts the modified algorithm proposed by Xie et al. which leads to higher accuracy and more physically reliable output parameters. A number of Monte Carlo simulations of polarimetric synthetic aperture radar (PolSAR) data are carried out and show that the proposed method with GVSM yields an overall improvement in the final accuracy of estimated parameters and outperforms both the version using SAVSM and the original approach. In addition, C-band Radarsat-2 and L-band AIRSAR fully polarimetric images over the San Francisco region are also used for testing purposes. A detailed comparison and analysis of decomposition results over different land-cover types are conducted. According to this study, the use of general decomposition models leads to a more accurate quantitative retrieval of target parameters. However, there exists a trade-off between parameter accuracy and model complexity which constrains the physical validity of solutions and must be further investigated.

In this paper, we analyze the robustness of the parameter inversion provided by general polarimetric model-based decomposition methods from the perspective of a quantitative application. The general model and algorithm we have studied is the method proposed recently by Chen et al., which makes use of the complete polarimetric information and outperforms traditional decomposition methods in terms of feature extraction from land covers. Nevertheless, a quantitative analysis on the retrieved parameters from that approach suggests that further investigations are required in order to fully confirm the links between a physically-based model (i.e., approaches derived from the Freeman–Durden concept) and its outputs as intermediate products before any biophysical parameter retrieval is addressed. To this aim, we propose some modifications on the optimization algorithm employed for model inversion, including redefined boundary conditions, transformation of variables, and a different strategy for values initialization. A number of Monte Carlo simulation tests for typical scenarios are carried out and show that the parameter estimation accuracy of the proposed method is significantly increased with respect to the original implementation. Fully polarimetric airborne datasets at L-band acquired by German Aerospace Center’s (DLR’s) experimental synthetic aperture radar (E-SAR) system were also used for testing purposes. The results show different qualitative descriptions of the same cover from six different model-based methods. According to the Bragg coefficient ratio (i.e., β ), they are prone to provide wrong numerical inversion results, which could prevent any subsequent quantitative characterization of specific areas in the scene. Besides the particular improvements proposed over an existing polarimetric inversion method, this paper is aimed at pointing out the necessity of checking quantitatively the accuracy of model-based PolSAR techniques for a reliable physical description of land covers beyond their proven utility for qualitative features extraction.

On the basis of the Gaussian vertical backscatter (GVB) model, this paper proposes a new method for extracting pine forest height and forest underlying digital elevation model (FUDEM) from multi-baseline (MB) P-band polarimetric-interferometric radar (PolInSAR) data. Considering the linear ground-to-volume relationship, the GVB is linked to the interferometric coherences of different polarizations. Subsequently, an inversion algorithm, weighted complex least squares adjustment (WCLSA), is formulated, including the mathematical model, the stochastic model and the parameter estimation method. The WCLSA method can take full advantage of the redundant observations, adjust the contributions of different observations and avoid null ground-to-volume ratio (GVR) assumption. The simulated experiment demonstrates that the WCLSA method is feasible to estimate the pure ground and volume scattering contributions. Finally, the WCLSA method is applied to E-SAR P-band data acquired over Krycklan Catchment covered with mixed pine forest. It is shown that the FUDEM highly agrees with those derived by LiDAR, with a root mean square error (RMSE) of 3.45 m, improved by 23.0% in comparison to the three-stage method. The difference between the extracted forest height and LiDAR forest height is assessed with a RMSE of 1.45 m, improved by 37.5% and 26.0%, respectively, for model and inversion aspects in comparison to three-stage inversion based on random volume over ground (RVoG) model.

Shenzhen, the first special economic zone of China, has witnessed earth-shaking changes since the late 1980s. In the past 35 years, about 80 km2 of land has been reclaimed from the sea in Shenzhen. In order to investigate coastal vertical land motions associated with land reclamation, we proposed an elaborated Point Target (PT) based Small Baseline Subset InSAR (SBAS-InSAR) strategy to process an ENVISAT ASAR ascending and descending orbits dataset both acquired from 2007 to 2010. This new strategy can not only select high density PTs but also generate a reliable InSAR measurement with full spatial resolution. The inter-comparison between InSAR-derived deformation velocities from different orbits shows a good self-consistency of the results extracted by the elaborated PT-based SBAS-InSAR strategy. The InSAR measurements show that the reclaimed land is undergoing remarkable coastal subsidence (up to 25 mm/year), especially at the Shenzhen Airport, Bao’an Center, Qianhai Bay and Shenzhen Bay. By analyzing the results together with land reclamation evolution, we conclude that the ground deformation is expected to continue in the near future, which will amplify the regional sea level rise.

Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio.