IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第4页

Tailings Ponds Detection in High-Resolution Remote Sensing Images Based on Irregular Attention Features 基于不规则注意特征的高分辨率遥感影像尾矿库检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-03-02 DOI: 10.1109/LGRS.2026.3669745

Jingyi Ding;Shilin Zhuang;Ailong Ma;Lyuzhou Gao;Yanfei Zhong

{"title":"Tailings Ponds Detection in High-Resolution Remote Sensing Images Based on Irregular Attention Features","authors":"Jingyi Ding;Shilin Zhuang;Ailong Ma;Lyuzhou Gao;Yanfei Zhong","doi":"10.1109/LGRS.2026.3669745","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3669745","url":null,"abstract":"As one of the main industrial wastes generated by mining activities, tailings ponds not only occupy a large amount of land resources, but also have the potential risk of causing environmental pollution and geological disasters. Therefore, achieving automatic identification of tailings ponds is vital for resource supervision and ecological protection. However, due to the inherent morphological and textural complexity of tailings ponds in remote sensing images, existing general-purpose object detectors struggle to achieve accurate detection. To meet these challenges, this letter proposes a tailings ponds detection framework based on irregular attention features, which is targeted at both positioning and classification. Specifically, this letter introduces a context-aware location refinement module, which uses position encoding to serialize images to improve the positioning accuracy of irregular targets; and a part attention classification refinement module is designed to focus on the most discriminative local areas to achieve more effective texture feature extraction and classification performance improvement. For evaluation purposes, we constructed a remote sensing dataset containing tailings ponds and buildings. Compared with existing benchmark methods, our method achieves satisfactory performance in the tailings ponds detection task, providing a reliable technical support for efficient and automated monitoring of tailings ponds.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148492288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robust Direct Path Interference Suppression Algorithm Based on Adaptive Component Extraction for GNSS-Based InBSAR System 基于自适应分量提取的gnss InBSAR鲁棒直径干扰抑制算法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-04-27 DOI: 10.1109/LGRS.2026.3687633

Feifeng Liu;Yunxuan Duan;Zhanze Wang;Jingtian Zhou

{"title":"A Robust Direct Path Interference Suppression Algorithm Based on Adaptive Component Extraction for GNSS-Based InBSAR System","authors":"Feifeng Liu;Yunxuan Duan;Zhanze Wang;Jingtian Zhou","doi":"10.1109/LGRS.2026.3687633","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3687633","url":null,"abstract":"Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) employs on-orbit navigation satellites as transmitters for interferometric processing. However, due to the use of C/A codes of navigation satellites, which do not decay in range direction after pulse compression, significant direct path interference (DPI) is introduced in SAR images. Moreover, the low power of navigation signals further complicates the suppression of DPI. In this letter, a robust DPI suppression algorithm based on adaptive component extraction is proposed for GNSS-based InBSAR. First, a signal model of the DPI is established, and a coarse reconstruction of the DPI is achieved through parameter estimation after azimuth accumulation. Subsequently, based on the reconstructed DPI, an adaptive extraction of the direct path component from the echo signal is performed to suppress the DPI while preserving the target echo. Using raw data from BeiDou satellites, the proposed algorithm improves the signal-to-interference ratio (SIR) from 8.27 to 16.11 dB and enhances the deformation accuracy of the interference area by 58.7%.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"4008005-4008005"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148493142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prompt-Calibrated SAM 3 for Open-Vocabulary Remote-Sensing Semantic Segmentation 面向开放词汇遥感语义分割的即时校准SAM 3

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-14 DOI: 10.1109/LGRS.2026.3713378

Yanghui Song;Nanqing Liu;Haonan Yin;Yingjie Gao;Chengfu Yang;Qi Ming

{"title":"Prompt-Calibrated SAM 3 for Open-Vocabulary Remote-Sensing Semantic Segmentation","authors":"Yanghui Song;Nanqing Liu;Haonan Yin;Yingjie Gao;Chengfu Yang;Qi Ming","doi":"10.1109/LGRS.2026.3713378","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3713378","url":null,"abstract":"Open-vocabulary semantic segmentation (OVSS) in remote-sensing images aims to segment categories beyond a fixed label space. Recent SAM3-based methods provide a promising training-free foundation; yet, three key issues remain: 1) a single class-name prompt lacks sufficient semantic coverage for complex remote-sensing categories; 2) expanding each category into multiple prompts introduces redundant online text encoding; and 3) directly aggregating multiple prompt responses propagates noisy activations into the final prediction. To address these issues, we propose ProC-SAM3, which calibrates SAM3’s prompt interface for remote-sensing OVSS from three complementary aspects. First, we construct an offline prompt pool where a category matcher (CM) groups MLLM-generated candidates into per-category sets, and expansion constraints further refine each set using category-specific prior knowledge. Second, the resulting text embeddings are cached and reused across all test images, eliminating repeated text encoding. Third, we introduce presence-guided residual fusion (PGRF) to gate unreliable decoder outputs by prompt presence and confidence, followed by peak-preserving class aggregation that retains fine-grained activations for small and sparse objects. Experiments on eight benchmarks show that ProC-SAM3 achieves an average mIoU of 56.1%, outperforming the previous best training-free method by 3.9 percentage points. Code will be available at <uri>https://github.com/YanghuiSong/ProC-SAM3</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"6015205-6015205"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Removing the Influences of Sun–Moon Distance and Subsatellite Position on Lunar Microwave Data From Microwave Humidity Sounder With Radiative Transfer Model 利用辐射传输模型消除日月距离和星下位置对微波湿度测深仪月球微波数据的影响

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-15 DOI: 10.1109/LGRS.2026.3713885

Niutao Liu;Martin Burgdorf;Ya-Qiu Jin

{"title":"Removing the Influences of Sun–Moon Distance and Subsatellite Position on Lunar Microwave Data From Microwave Humidity Sounder With Radiative Transfer Model","authors":"Niutao Liu;Martin Burgdorf;Ya-Qiu Jin","doi":"10.1109/LGRS.2026.3713885","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3713885","url":null,"abstract":"The microwave emission of the Moon varies periodically, which is a potential calibration source for the meteorological satellites. The microwave humidity sounder (MHS) onboard NOAA-18 measured the disk-integrated microwave brightness temperature (TB) of the Moon. The position of the subsatellite point and the distance between the Sun and the Moon will affect the observed TB data. These factors will increase the deviation between the data and simulations. In order to eliminate these differences, the influence of the position of the subsatellite point and the Sun–Moon distance on the disk-integrated TB are simulated at 89, 157, 183, and 190 GHz with a radiative transfer model. Based on the simulations, the influences of the subsatellite point and the Sun–Moon distance on the observed data are removed. After removing these effects, the root-mean-square errors (RMSEs) between the 89, 157, 183, and 190-GHz data and the simulations are reduced by about 0.22–0.35 K. Reducing the difference between the data and the model can promote the application of lunar microwave calibration.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"4500905-4500905"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MUF Extraction From Oblique Ionogram Based on Keypoint Detection 基于关键点检测的斜离子图MUF提取

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-02 DOI: 10.1109/LGRS.2026.3709300

Zhongjin Yang;Guobin Yang;Tongxin Liu;Chunhua Jiang;Erpan Yi;Kaihua Chen

引用次数: 0

Mamba-Based Frequency-Domain Enhancement Infrared Small Target Detection Network 基于曼巴的频域增强红外小目标探测网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-13 DOI: 10.1109/LGRS.2026.3711990

Tie Zhong;Qing Yang;Xinyang Wang

{"title":"Mamba-Based Frequency-Domain Enhancement Infrared Small Target Detection Network","authors":"Tie Zhong;Qing Yang;Xinyang Wang","doi":"10.1109/LGRS.2026.3711990","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3711990","url":null,"abstract":"Infrared small target detection (IRSTD) remains a formidable challenge due to the low illumination, low target–background contrast, and indistinct target characteristics, which often lead to severe missed detections. We propose a Mamba-based frequency-domain enhanced network, termed Mamba-based frequency-domain enhancement infrared small target detection network (MFDENet). Specifically, to enhance target contrast and preserve fine-grained details, we propose a dual-domain feature complementary module (DD-FCM), which dynamically fuses complementary information from the original and enhanced domains. In addition, to utilize frequency-domain priors, we introduce a Mamba-based frequency-domain feature extractor that separates noise patterns from target components in the frequency domain and captures long-range dependencies among frequency coefficients. By integrating contrast-enhanced spatial features with robust frequency-domain representations, MFDENet improves the detection of weak infrared targets. Experiments on multiple datasets indicate that MFDENet achieves competitive mean intersection over union (mIoU) and exhibits superior detection accuracy over existing methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"6015105-6015105"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phenology-Archetype-Driven Reconstruction of Daily High-Resolution NDVI Time Series by Fusing Tower Observations and GF Satellite Imagery 基于物候原型驱动的日高分辨率NDVI时间序列的塔观测和GF卫星图像融合重建

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-09 DOI: 10.1109/LGRS.2026.3711651

Xiangyu Xiao;Yadong Dong;Jing Li;Jing Zhao;Jixi Gao;Wenming Shen;Shangrong Lin;Junyong Fang;Qing Xiao;Qian Luo;Wangzehao Wu;Qinhuo Liu

{"title":"Phenology-Archetype-Driven Reconstruction of Daily High-Resolution NDVI Time Series by Fusing Tower Observations and GF Satellite Imagery","authors":"Xiangyu Xiao;Yadong Dong;Jing Li;Jing Zhao;Jixi Gao;Wenming Shen;Shangrong Lin;Junyong Fang;Qing Xiao;Qian Luo;Wangzehao Wu;Qinhuo Liu","doi":"10.1109/LGRS.2026.3711651","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3711651","url":null,"abstract":"The normalized difference vegetation index (NDVI) is widely used for vegetation monitoring, phenological analysis, and ecosystem assessment. However, obtaining continuous daily high-resolution NDVI time series remains challenging because high-resolution satellite observations are often affected by long revisit intervals and cloud contamination. Existing reconstruction methods generally rely on dense observations or auxiliary datasets, limiting their applicability in data-sparse regions. In this study, we propose a method for extracting phenological archetypes from tower observations to reconstruct the GF NDVI time series. The proposed method successfully reconstructed daily NDVI for 2024 over a forested area. The results show that the reconstructed NDVI achieves an RMSE of 0.100 at 16-m resolution (<inline-formula> <tex-math>$R^{2}=0.942$ </tex-math></inline-formula>) and 0.158 at 1.5-m resolution (<inline-formula> <tex-math>$R^{2}=0.789$ </tex-math></inline-formula>), compared with tower observations. The method enhances spatial detail by more than five times relative to the original GF NDVI while maintaining temporal consistency, requiring only tower observations and limited satellite data. These results demonstrate the potential of the proposed approach for high-resolution daily NDVI reconstruction in data-sparse regions.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"2505205-2505205"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148628109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Structured Constraint-Based RPCA Algorithm for Ground Moving Target Detection and Parameter Estimation 一种基于结构化约束的RPCA算法用于地面运动目标检测与参数估计

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-15 DOI: 10.1109/LGRS.2026.3713514

Kun Liu;Xiongpeng He;Guisheng Liao;Jian Xue;Biao Yang

{"title":"A Structured Constraint-Based RPCA Algorithm for Ground Moving Target Detection and Parameter Estimation","authors":"Kun Liu;Xiongpeng He;Guisheng Liao;Jian Xue;Biao Yang","doi":"10.1109/LGRS.2026.3713514","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3713514","url":null,"abstract":"Robust principal component analysis (RPCA) exploits target characteristics in multichannel synthetic aperture radar ground moving target indication (SAR-GMTI) to decompose the observed data matrix into a low-rank clutter matrix and a sparse target matrix. However, during the optimization process, conventional RPCA algorithms may distort the target phase information, resulting in the separation of target detection and parameter estimation. In addition, strong scatterers may lead to a high false alarm rate in target detection. To address these challenges, a structured constraint-based RPCA (SCRPCA) algorithm is proposed. On the one hand, the rank-one property of clutter is utilized to replace the conventional low-rank constraint; on the other hand, the sparse target component is reformulated as the product of amplitude and phase terms, enabling simultaneous target detection and parameter estimation. Furthermore, the along-track interferometry (ATI) phase is incorporated as a weighting factor to suppress clutter energy leakage. The block coordinate descent (BCD) algorithm is employed to solve the proposed optimization model. Experimental results using real measured data demonstrate the effectiveness of the proposed method.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"3502505-3502505"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Remotely Sensed Data-Driven Deep Learning Assimilation for Wave in China Seas 基于遥感数据的中国海域波浪深度学习同化研究

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-29 DOI: 10.1109/LGRS.2026.3718320

Lu Liu;Weizeng Shao;Xingai Song;Xingwei Jiang;Song Pan

{"title":"Remotely Sensed Data-Driven Deep Learning Assimilation for Wave in China Seas","authors":"Lu Liu;Weizeng Shao;Xingai Song;Xingwei Jiang;Song Pan","doi":"10.1109/LGRS.2026.3718320","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3718320","url":null,"abstract":"This letter proposes a U-shaped network (Unet)-based postprocessing data-assimilation framework to improve hourly significant wave height (SWH) hindcasts from the finite-volume community ocean model (FVCOM)–simulating waves nearshore (SWAN) in the China Seas. Unet is trained with 2024 SWH observations from Jason-3, Haiyang-2 (HY-2), and the surface waves investigation and monitoring (SWIM) instrument onboard the China–France Oceanography Satellite (CFOSAT), and applied to the 2014–2023 hindcasts. Validation against buoy observations during 2022–2023 shows that Unet reduces the root-mean-squared error (RMSE) from 0.65 to 0.53 m and increases the correlation coefficient (Cor) from 0.84 to 0.86. Unet also outperforms residual Unet (ResUNet), the Kalman filter (KF), and ensemble optimal interpolation (EnOI), whose RMSEs are 0.58, 0.63, and 0.64 m, respectively. Monthly mean SWH from the assimilated fields shows positive tendencies during 2014–2023, demonstrating the framework’s potential for regional decadal-scale variability analysis.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1504705-1504705"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-Based Relational Learning for Building Change Detection 基于图的关系学习用于建筑变更检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-07-30 DOI: 10.1109/LGRS.2026.3718399

Ahram Song;Seula Park

{"title":"Graph-Based Relational Learning for Building Change Detection","authors":"Ahram Song;Seula Park","doi":"10.1109/LGRS.2026.3718399","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3718399","url":null,"abstract":"Recent graph-based change detection (CD) methods either encode relationships implicitly or rely on post hoc matching, limiting the explicit modeling of object-level spatial and topological relationships under geometric distortions. To address this issue, we propose a CD framework based on a rasterized graph feature map (GFM) that encodes node- and edge-level relationships and is integrated with RGB imagery. The framework incorporates relational feature modulation and anisotropic relational modeling to capture orientation-dependent interactions between buildings, and a graph-aware composite loss is designed to guide relationally consistent predictions. Experiments on the BANDON dataset demonstrate that the proposed method improves the overall CD performance, achieving higher <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score and mIoU with a reduced false positive rate under challenging off-nadir conditions. Ablation studies further show that node- and edge-level cues are most effective when jointly represented as a full GFM and that graph-aware losses contribute to improving prediction reliability by suppressing spurious responses.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"6015905-6015905"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148749188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0