IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"A Multimodal Semantic Segmentation Framework for Heterogeneous Optical and Complex SAR Data","authors":"Sining Xiao;Peijin Wang;Wenhui Diao;Kun Fu;Xian Sun","doi":"10.1109/JSTARS.2025.3542487","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542487","url":null,"abstract":"The advancement of remote sensing technology has led to a progressive enhancement in the resolution of remote sensing data, offering a multiperspective approach to Earth observation and facilitating a more comprehensive scene interpretation. As two most commonly utilized data sources in remote sensing, optical images, and synthetic aperture radar (SAR) data can provide complementary information, effectively compensating for the limitations inherent to a single modality. However, existing methods for using these two data sources face the following issues. First, insufficient utilization of the complete information provided by the source data. Second, inadequate consideration of the distinct characteristics of different modalities during feature extraction. Third, ignoring the misalignment between heterogeneous data, leading to large information loss. To tackle these challenges, we initially construct a benchmark dataset comprising complex-valued SAR data and optical images, named Multi-Complex-Seg. In order to fully mine the complete and valid information provided by both data sources, we construct a multimodal segmentation framework built on the theory of “subdomain extraction and cross-domain fusion,” in which we design a more suitable feature extractor for complex-valued SAR data, fully considering the unique geometric properties. In addition, a dynamic feature alignment module (DFAM) is proposed to further adjust the cross-modal features, and Cross-modal heterogeneous feature fusion module (CHFFM) first maps features into the same latent space to obtain better fused features. Both DFAM and CHFFM together reduce the huge semantic gap between modalities, thus facilitating the extraction of intramodal specificity and cross-modal complementarity. Extensive experiments on the proposed Multi-Complex-Seg confirm the effectiveness of our framework in comparison to other state-of-the-art multimodal segmentation approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8083-8098"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ocean Surface Retracking in Tropical Cyclones With the KaIA Airborne Radar Altimeter","authors":"Clayton M. Bjorland;Zorana Jelenak;Joseph W. Sapp;Casey G. Shoup;Bradley M. Isom;Paul S. Chang;James R. Carswell","doi":"10.1109/JSTARS.2025.3542635","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542635","url":null,"abstract":"KaIA is an airborne Ka-band radar altimeter capable of centimetric range resolution and near real-time significant wave height retrievals. Beginning in 2019, KaIA has been installed on National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter aircraft to collect data in Atlantic tropical cyclones during the hurricane season, and extratropical cyclones during the winter storm season. This article details recent retracker algorithm innovations that address specific difficulties with airborne altimetry in extreme weather. Our two most significant contributions to retracker algorithm development are: 1) a higher-order expansion of the classic Brown (1977) altimetry waveform to accommodate off-nadir pointing angles up to 3.25<inline-formula><tex-math>${}^{circ }$</tex-math></inline-formula>; 2) a GPS stabilization algorithm to enable along-track averaging while aircraft altitude is changing unpredictably. Comparisons against coincident measurements and modeled data are presented to validate algorithm improvements and document KaIA's performance throughout the 2021-2023 hurricane seasons. We measure less than 0.1 m bias in significant wave height retrievals relative to coincident satellite altimeters.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6480-6491"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Dual-Strategy Learning Framework for Hyperspectral Image Super-Resolution","authors":"Shuying Li;Ruichao Sun;San Zhang;Qiang Li","doi":"10.1109/JSTARS.2025.3542766","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542766","url":null,"abstract":"Hyperspectral image super-resolution (HSI SR) has achieved remarkable success with deep neural networks. Currently, most methods in HSI SR assume a predetermined degradation model during training to synthesize low-resolution images. These methods falter when confronted with HSI exhibiting degradation patterns and their limited flexibility restricts practical application. In addition, these methods focus on the complex network designs for superior performance, which entail high resource consumption and limit their broad application. To address these issues, in this article, we propose a dual-strategy learning framework exploring meta-transfer learning for HSI blind SR. This framework can be applied to any SR network and facilitate performance enhancement. First, we pretrain a three-channel SR model on natural image data to address the issue of insufficient HSI data. Furthermore, we innovatively propose a transfer scheme, which directly applies our pretrained three-channel SR model to HSI, thereby significantly enhancing the spectral fidelity. To enhance the model's performance under specific degradation conditions, we incorporate meta-learning, enabling it to adapt to input images after a few iterations. Besides, we introduce attention-based knowledge distillation to equip our final network with the implicit representation capability of a meta network under a lightweight premise. Extensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms existing methods in various degradations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7480-7494"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GANFlow: A Hybrid Model for SAR Image Target Open-Set Recognition Based on GAN and the Flow-Based Module","authors":"Jikai Qin;Jiusheng Han;Zheng Liu;Lei Ran;Rong Xie;Tat-Soon Yeo","doi":"10.1109/JSTARS.2025.3542738","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542738","url":null,"abstract":"Most synthetic aperture radar (SAR) automatic target recognition (ATR) methods can achieve good recognition results only under the closed-set assumption. However, in practical applications, ATR models are often exposed to open environments, the general closed-set method may misclassify unknown categories as known categories, which is not reasonable. To tackle this issue, this article proposes an end-to-end hybrid model for SAR image open-set recognition (OSR), named GANFlow, which combines a generative adversarial network (GAN) with a flow-based module. The GANFlow achieves accurate classification of known categories and effective rejection of unknown categories. In this model, a classifiable convolution GAN is first designed to complete the training of the feature extraction module and classifier. Through adversarial training, the generated images enrich the training samples, which improves the ability of feature extraction and classification of the discriminator. Then, to find the difference in the probability density distribution of the extracted features, a flow-based module is adopted. Also, the features avoid interference from irrelevant background information in SAR images. Furthermore, by establishing an appropriate threshold, unknown categories can be efficiently rejected. Finally, the outputs of the classifier and the flow-based module are combined to complete the OSR of the SAR image target. The experimental results on the MSTAR and OpenSARShip public-measured datasets verify the robustness and generalization of the proposed method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7083-7099"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Data-Optimized Oblique Mercator Projection","authors":"Sebastian von Specht;Malte J. Ziebarth","doi":"10.1109/JSTARS.2025.3542802","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542802","url":null,"abstract":"Map projections transform the Earth's curved surface into a plane and are thus crucial for mapping and geospatial analysis. However, projections inevitably introduce distortion, requiring the selection of a suitable map projection for the mapped region. The conventional approach is to choose from predefined map projections. Unfortunately, the available projections are limited in variety and can be difficult to evaluate effectively. We propose an alternative approach: rather than selecting from a predefined set of projections, we introduce an algorithm that optimizes a single projection for a given dataset: Data-Optimized Oblique Mercator (DOOM). At its core is the HOM projection, featuring a flexible set of adjustable parameters and a universal implementation in GIS platforms and related software. DOOM utilizes the well-established optimization algorithms Levenberg–Marquardt, Adamax, and BFGS, to optimize the projection parameters, minimizing distortion in the mapping of geospatial data. The algorithm supports various objective functions (e.g., <inline-formula><tex-math>$L^{1}$</tex-math></inline-formula>- and <inline-formula><tex-math>$L^{2}$</tex-math></inline-formula>-norms, minmax) and can be extended to incorporate data weighting. The methodology is validated through several case studies, highlighting its adaptability across diverse applications. In addition, we introduce a GIS plugin to streamline the use of optimized projection parameters, enhancing accessibility for the geospatial community.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6916-6939"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Fast Sparse Unmixing Algorithm Based on Adaptive Spectral Library Pruning and Nesterov Optimization","authors":"Kewen Qu;Fangzhou Luo;Huiyang Wang;Wenxing Bao","doi":"10.1109/JSTARS.2025.3541257","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3541257","url":null,"abstract":"In recent years, hyperspectral sparse unmixing (HSU) has garnered extensive research and attention due to its unique characteristic of not requiring the estimation of endmembers and their number. However, the high coherence and large-scale nature of the prior spectral library frequently lead to substantial computational costs and limited unmixing accuracy in the optimization model, thereby hindering the efficiency and further promotion of HSU in practical engineering applications. To address these shortcomings, this article proposes a new fast two-step sparse unmixing algorithm, called NeSU-LP, which is based on adaptive spectral library pruning technology and the Nesterov fast optimization strategy. In this method, HSU is divided into two independent and consecutive subprocesses: coarse unmixing and fine unmixing. Specially, first, in the coarse unmixing stage, we design a sparse optimization model based on the initial large spectral library, requiring only a few iterations to initially estimate the row-sparse abundance matrix. Subsequently, the proposed atomic (i.e., endmember) activity evaluation method is utilized to screen the active endmembers, analyze the abundance matrix, and prune the endmembers in the spectral library. Irrelevant endmembers are removed, reducing the spectral library size and generating a low-coherence, small-scale endmember matrix. Finally, in the fine unmixing process, we retain the effective atomic abundance rows obtained in the previous stage and design the final fine hyperspectral unmixing model based on the pruned, small-scale endmember matrix. In addition, to enhance the smoothness of the abundance maps, graph Laplacian regularization is introduced during the fine unmixing stage. The Nesterov fast gradient strategy is employed to accelerate the iterative process of fine unmixing, ultimately achieving second-order convergence efficiency for the algorithm. Numerous experiments were conducted on both synthetic and real datasets, comparing them with state-of-the-art methods. The experimental results demonstrate the high efficiency and advancement of the proposed method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6134-6151"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Correlations Sparse Bayesian Learning STAP With Prior Knowledge of Clutter Ridge","authors":"Junhao Cui;Zhangxin Chen;Jing Liang","doi":"10.1109/JSTARS.2025.3542421","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542421","url":null,"abstract":"Space-time adaptive processing (STAP) based on sparse Bayesian learning (SBL) can significantly improve clutter suppression performance utilizing clutter sparsity. However, the existing SBL-STAP algorithms lack full use of correlations, which leads to unsatisfactory performance and slow convergence speed. In this article, we propose a joint correlations SBL-STAP (JCSBL-STAP) algorithm to improve clutter suppression performance. It comes from a rational idea that the clutter ridge in the space-time domain is not only the origin of clutter sparsity, but also the origin of correlations. Normally, the amplitude of scatterers along the clutter ridge are correlated between multiple samples and have clustered correlation properties in each sample. The JCSBL-STAP algorithm utilizes a joint correlations sparse prior to exploiting both correlations and provides a multisample correlation decoupling framework to update hyperparameters. The algorithm is executed on a proposed hybrid prior dictionary. Compared with the conventional uniform dictionary, the hybrid prior dictionary can easily express the clustered correlation properties and effectively alleviates the off-grid problem. Experimental results confirm the performance of the proposed method on both simulated data and measured Mountain-Top data.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6820-6832"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10890973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SIFANet: Spatial-Temporal Interaction and Frequency Adaptive Awareness Network for Change Detection in Remote Sensing Images","authors":"Jia Liu;Kaixuan Jiang;Wenhua Zhang;Fang Liu;Liang Xiao;Puzhao Zhang;Chen Wu","doi":"10.1109/JSTARS.2025.3542469","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542469","url":null,"abstract":"Change detection (CD) is an essential mission in the realm of remote sensing. In previous years, deep learning has been introduced into the domain of CD and has made great progress. How to effectively utilize useful information to improve detection performance remains a challenge. To alleviate this concern, we propose a network based on spatial-temporal interaction and frequency adaptive awareness. The network contains three main modules. Specifically, we design a spatial-temporal interaction module that enhances the interaction of disparity features with diachronic features to intensify the focus on change regions. Subsequently, in the decoding phase, we use deep features to guide the shallow feature generation, which can effectively filter the background clutter of shallow features, where an adaptive upsampling module is implemented for effective feature fusion. Finally, frequency adaptive awareness module is utilized for modeling multiscale features by combining frequency domain and temporal domain features, thus enhancing the model's ability to perceive changed regions. We have performed experiments over three prevalent datasets CDD, SYSU-CD, and LEVIR-CD, respectively. The proposed method achieves IoU of 95.70% (4.92% improvement over secondary one) on the CDD dataset, 84.34% (1.94% improvement over secondary one) with LEVIR-CD dataset, and 69.89% (0.22% improvement over secondary one) for SYSU-CD dataset. Our approach outperforms other state-of-the-art CD methods. Visible results indicate that our method generates more complete and clearer details of the changes.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6654-6667"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10890992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection","authors":"Degang Wang;Longfei Ren;Xu Sun;Lianru Gao;Jocelyn Chanussot","doi":"10.1109/JSTARS.2025.3542457","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542457","url":null,"abstract":"Hyperspectral anomaly detection (HAD) aims to locate targets deviating from the background distribution in hyperspectral images (HSIs) without requiring prior knowledge. Most current deep learning-based HAD methods struggle to effectively distinguish anomalies due to limited utilization of supervision information and intrinsic nonlocal self-similarity in HSIs. To this end, this article proposes a novel nonlocal and local feature-coupled self-supervised network (NL2Net) tailored for HAD. NL2Net employs a dual-branch architecture that integrates both local and nonlocal feature extraction. The local feature extraction branch (LFEB) leverages centrally masked and dilated convolutions to extract local spatial-spectral features, while the non-LFEB incorporates a simplified self-attention module to capture long-range dependencies. Furthermore, an improved center block masked convolution strengthens NL2Net ’s focus on surrounding background features, enhancing the background modeling effect. By reconstructing pure backgrounds and suppressing anomalous features, NL2Net achieves precise anomaly separation and superior HAD performance. Experimental results demonstrate its ability to effectively integrate multidimensional features and enhance HAD accuracy, surpassing state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6981-6993"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10890991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery With Latent Diffusion","authors":"Simon Donike;Cesar Aybar;Luis Gómez-Chova;Freddie Kalaitzis","doi":"10.1109/JSTARS.2025.3542220","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542220","url":null,"abstract":"Remote sensing super-resolution aims to enhance the spatial details of satellite images by introducing meaningful high-frequency features while avoiding hallucinations and spectral distortions. High-resolution imagery is usually not publicly available, whereas low-resolution imagery is freely available with a much higher revisit rate, such as the Sentinel-2 multispectral imaging mission. Cross-sensor super-resolution has the potential to bridge this gap, providing high spatial and temporal resolution imagery which are otherwise unavailable for many remote sensing users and applications. With the recent advancements in diffusion models, many methodologies have emerged which take advantage of their generative power to perform super-resolution. We propose an adapted latent diffusion approach, since image diffusion is computationally prohibitive to be applied to large Earth observation datasets. Contrary to standard latent diffusion, we encode the low-resolution image to condition the diffusion process, forcing better spectral consistency with the input imagery. The model includes visible and near-infrared bands. To ensure trustworthy results, we utilize the probabilistic nature of diffusion models to generate pixel-level uncertainty maps. This confidence metric is crucial for real-world applications, such as environmental monitoring, land cover classification, and change detection, where accurate surface feature reconstruction and spectral consistency are essential. The uncertainty map allows users to evaluate the reliability of the product for these tasks. The proposed model super-resolves Sentinel-2 imagery at 10 to 2.5 m and is the first multispectral remote sensing (RS) super-resolution diffusion model efficient enough to process large-scale RS datasets, as well as the only model providing a pixelwise uncertainty metric.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6940-6952"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}