IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第10页

Evaluation of GPM DPR Precipitation Products After Orbit Boost Using Disdrometers in Italy 意大利轨道助推后GPM DPR降水产品的disdrometer评价

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: 2025-11-28 DOI: 10.1109/LGRS.2025.3638692

Sabina Angeloni;Elisa Adirosi;Luca Baldini;Mario Montopoli;Alessandro Bracci;Vincenzo Capozzi;Clizia Annella;Simone Scapin;Paolo Valisa;Lorenzo Luini;Roberto Nebuloni;Roberto Cremonini;Renzo Bechini;David B. Wolff

{"title":"Evaluation of GPM DPR Precipitation Products After Orbit Boost Using Disdrometers in Italy","authors":"Sabina Angeloni;Elisa Adirosi;Luca Baldini;Mario Montopoli;Alessandro Bracci;Vincenzo Capozzi;Clizia Annella;Simone Scapin;Paolo Valisa;Lorenzo Luini;Roberto Nebuloni;Roberto Cremonini;Renzo Bechini;David B. Wolff","doi":"10.1109/LGRS.2025.3638692","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3638692","url":null,"abstract":"Precipitation measurements are crucial for improving climate models and weather forecasting, with satellite-based radar systems, such as the dual precipitation radar (DPR) on board the Global Precipitation Measurement Core Observatory (GPM CO), playing a pivotal role in enhancing observational coverage, especially in remote regions. In November 2023, an orbit boost maneuver raised the GPM CO’s altitude from 400 to 435 km, potentially influencing the quality of DPR data. This study evaluates the impact of this altitude increase on the accuracy of DPR-derived precipitation products by comparing data acquired before and after the orbit boost. We assess the performance of DPR measurements against ground-based laser disdrometer data collected in Italy between May 2018 and November 2024. The results highlight the effects of the altitude change on rainfall retrievals, contributing to a better understanding of how orbital adjustments can affect satellite-based precipitation observations.","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":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148489333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MBE-Net: A Multibranch Edge-Guided Feature Enhancement Algorithm for SAR Ship Detection MBE-Net：一种多分支边缘引导的SAR舰船检测特征增强算法

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: 2026-03-09 DOI: 10.1109/LGRS.2026.3672324

Jinwei Wang;Zihao Wang;Guangcai Sun;Bo Zhao

{"title":"MBE-Net: A Multibranch Edge-Guided Feature Enhancement Algorithm for SAR Ship Detection","authors":"Jinwei Wang;Zihao Wang;Guangcai Sun;Bo Zhao","doi":"10.1109/LGRS.2026.3672324","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3672324","url":null,"abstract":"Synthetic aperture radar (SAR) imagery plays a vital role in maritime surveillance, yet it faces severe challenges such as speckle noise, cluttered backgrounds, and the small scale and low contrast of ship targets. To overcome these issues, we propose MBE-Net, a lightweight and high-accuracy SAR ship detection network featuring multibranch edge-guided feature enhancement. First, the edge-enhanced wavelet backbone (EEWB) is designed to enhance edge information and suppress detail loss by integrating multibranch edge guidance and wavelet pooling, thereby improving small-ship perception in complex scenes. Second, the lightweight decoupled detail-enhanced head (LD-DEH) decouples classification and regression tasks while embedding a detail-enhanced convolution module to recover high-resolution features, significantly boosting localization precision under strong interference. Finally, the adaptive contextual focus feature pyramid network (ACF-FPN) introduces context-guided fusion to improve multiscale feature alignment and robustness, especially in low-contrast and cluttered environments. Extensive experiments on the HRSID and SSDD datasets validate that MBE-Net achieves excellent detection accuracy with minimal computational cost, making it highly suitable for real-time SAR ship detection.","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":"148489384","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

Robust Registration of Multimodal Remote Sensing Images Using Steerable Hermite Filters and Geometric Constraints 基于可操纵Hermite滤波器和几何约束的多模态遥感图像鲁棒配准

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: 2025-12-17 DOI: 10.1109/LGRS.2025.3645106

Ruixiang Li;Mi Wang;Jun Pan;Yingdong Pi

{"title":"Robust Registration of Multimodal Remote Sensing Images Using Steerable Hermite Filters and Geometric Constraints","authors":"Ruixiang Li;Mi Wang;Jun Pan;Yingdong Pi","doi":"10.1109/LGRS.2025.3645106","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3645106","url":null,"abstract":"Co-registration of multimodal remote sensing images (MRSIs) remains a challenging task due to nonlinear radiometric differences (NRD) and geometric distortions. This letter proposes a robust registration method based on steerable Hermite filters and geometric constraints. The method consists of three stages: coarse registration, image matching, and fine registration. In the first stage, geo-referencing information is used to partition the overlapping region into subblocks for initial alignment. In the second stage, steerable Hermite filters are applied to extract multiscale and multiorientation structural features, which are fused into a principal moment map to detect highly repeatable feature points. A structural descriptor is subsequently constructed from an orientation index map (OIM) generated through hierarchical pooling, enabling robust matching under NRD. In the final stage, an iterative optimization strategy incorporating geometric constraints is employed to eliminate mismatches and refine the registration. Experimental results on multiple MRSI datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both matching robustness and registration accuracy.","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":"148489751","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

NH-CD2: Neck-Head Combined Double-Branch Distillation for Generalized Few-Shot Object Detection NH-CD2：颈-头联合双分支精馏广义少弹目标检测

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: 2026-06-17 DOI: 10.1109/LGRS.2026.3704624

Jing Zhang;Sisi Huang;Zhaolong Hong;Xinyi Yu;Yunsong Li

{"title":"NH-CD2: Neck-Head Combined Double-Branch Distillation for Generalized Few-Shot Object Detection","authors":"Jing Zhang;Sisi Huang;Zhaolong Hong;Xinyi Yu;Yunsong Li","doi":"10.1109/LGRS.2026.3704624","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3704624","url":null,"abstract":"To address the issue of catastrophic forgetting in generalized few-shot object detection (FSOD) for remote sensing images. This letter introduces an innovative method based on the distillation of a double branch. The proposed framework, named neck-head combined double-branch distillation (NH-CD2), is based on the YOLOv11 single-stage detector and includes two essential components. We propose head double-branch response distillation (HDRD) in the fine-tuning stage of the detection head. Regression and classification responses from both the base and novel branches are employed simultaneously as teacher knowledge. The mean squared error (mse) loss is used to facilitate students in quickly acquiring the detection capabilities for novel classes while mitigating the catastrophic forgetting of the base classes. In addition, we introduce neck-projection purification feature distillation (NPFD), where the middle-layer features act as knowledge. The base branch acts as the teacher, using its features to guide the students in retaining the characteristics of the base class. The novel branch implements a purification operation on the features to extract components specific to the novel class that differ from the base class. Subsequently, the students are guided to learn the enhancement features of the novel class. Comparative studies and ablation experiments on the NWPUv2 remote sensing datasets further confirm that the proposed method outperforms existing state-of-the-art techniques.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"7504805-7504805"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148490067","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

SCAFNet: A Semantic Compensated Adaptive Fusion Network for Remote Sensing Images Change Detection 基于语义补偿的遥感图像自适应融合网络SCAFNet

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 DOI: 10.1109/LGRS.2025.3650414

Yunzuo Zhang;Jiawen Zhen;Shibo Sun;Ting Liu;Lei Huo;Tong Wang

{"title":"SCAFNet: A Semantic Compensated Adaptive Fusion Network for Remote Sensing Images Change Detection","authors":"Yunzuo Zhang;Jiawen Zhen;Shibo Sun;Ting Liu;Lei Huo;Tong Wang","doi":"10.1109/LGRS.2025.3650414","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3650414","url":null,"abstract":"Current CNN–Transformer hybrid methods for remote sensing change detection aim to address the limitations of the CNNs’ constrained receptive fields and the Transformers’ local detail insensitivity. However, these methods suffer from semantic misalignment and nonadaptive fusion between the dual branches, resulting in persistent sensitivity to pseudo-changes. To address these issues, we propose SCAFNet, a semantic compensated adaptive fusion network, featuring three core components: 1) the semantic compensation module (SCM) that aligns local–global features via cross-attention to resolve spatial–semantic mismatches; 2) the CNN–Transformer feature adaptive fusion (CTFAF) module improving feature integration by dynamically balancing the local details of the CNN and the global context of the Transformer through cross-branch attention interactions and dynamic parameterization; and 3) the change feature identification module (CFIM) computing channel and spatial weights, enhancing true changes while suppressing disturbances such as seasonal variations. Experiments on CDD and WHU-CD datasets demonstrate SCAFNet’s superior robustness and accuracy, outperforming existing methods through effective feature fusion. The source code and supplementary materials will be made available at <uri>https://github.com/Gyroprime/SCAFNet</uri>","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":"148490623","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

PhysDNet: Physics-Guided Decomposition Network of Side-Scan Sonar Imagery 物理引导的侧扫声纳图像分解网络

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: 2026-06-18 DOI: 10.1109/LGRS.2026.3704815

Can Lei;Hayat Rajani;Nuno Gracias;Rafael Garcia;Huigang Wang

{"title":"PhysDNet: Physics-Guided Decomposition Network of Side-Scan Sonar Imagery","authors":"Can Lei;Hayat Rajani;Nuno Gracias;Rafael Garcia;Huigang Wang","doi":"10.1109/LGRS.2026.3704815","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3704815","url":null,"abstract":"Side-scan sonar (SSS) has become an essential tool for seafloor mapping and underwater exploration. However, the measured intensity is entangled with the seabed reflectivity, terrain elevation, and acoustic path loss, making the imagery highly view-dependent and hindering robust interpretation. To address this challenge, we propose PhysDNet, a physics-guided multibranch network that decomposes SSS images into three interpretable fields: effective seabed backscatter, terrain elevation, and attenuation. The network employs attention-augmented encoder–decoder branches, imposes rangewise consistency constraints on the attenuation field, and incorporates a Lambertian-based reconstruction to enable progressive self-supervised training, thereby ensuring stable multitask optimization while embedding physical priors into the learning process. Experiments demonstrate that the decomposed fields preserve geological structures, recover physically consistent illumination and attenuation, and enable accurate shadow prediction. These findings confirm that physics-guided decomposition establishes a stable and interpretable domain for SSS analysis, enhancing both physical consistency and downstream applications such as shadow interpretation.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1503505-1503505"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148490894","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

PAF-Net: Prototype-Guided Attention Fusion Network for HSI and LiDAR Data Joint Classification 用于HSI和LiDAR数据联合分类的原型引导注意力融合网络

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: 2026-03-13 DOI: 10.1109/LGRS.2026.3692786

Fanbin Zhang;Xiaoguang Yuan;Wei Feng

{"title":"PAF-Net: Prototype-Guided Attention Fusion Network for HSI and LiDAR Data Joint Classification","authors":"Fanbin Zhang;Xiaoguang Yuan;Wei Feng","doi":"10.1109/LGRS.2026.3692786","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3692786","url":null,"abstract":"Hyperspectral images (HSIs) and light detection and ranging (LiDAR) data are highly complementary at the semantic level, and their fusion contributes to more accurate image classification. However, existing fusion mechanisms are often limited by unconstrained feature interactions and imbalanced learning rates across heterogeneous modalities. In this letter, a prototype-guided attention fusion network (PAF-Net) is proposed, which employs category prototypes as alignment targets to enhance feature learning and attention fusion. In particular, a convolutionally aligned cross-modal transformer (CACMT) models local–global spatial relationships to facilitate cross-modal interaction. A confidence-weighted modality masking (CWMM) module then adaptively adjusts modality channels to ensure comprehensive feature learning for all modalities. Finally, a semantic-query-guided cross-attention fusion (SQ-CAF) module exchanges semantic cues between modalities to fully mine their complementary information. Extensive experiments on three mainstream HSI and LiDAR datasets demonstrate the effectiveness of the proposed PAF-Net.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"5504705-5504705"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148491008","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

EDTNet: An Edge-Guided Dual-Branch Transformer Network for Mars Layered Ejecta Craters Extraction EDTNet：一种边缘导向的双支路变压器网络，用于火星分层弹射坑提取

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: 2026-04-06 DOI: 10.1109/LGRS.2026.3681022

Dongchen Di;Shiyang Fu;Zhizhong Kang

{"title":"EDTNet: An Edge-Guided Dual-Branch Transformer Network for Mars Layered Ejecta Craters Extraction","authors":"Dongchen Di;Shiyang Fu;Zhizhong Kang","doi":"10.1109/LGRS.2026.3681022","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3681022","url":null,"abstract":"Layered ejecta craters (LECs) are distinctive Martian craters with fluidized ejecta whose morphological characteristics correlate with Martian liquid water and the distribution of subsurface water ice. Existing crater extraction methods struggle to effectively extract LECs with complex edges or significantly degraded boundaries and fail to sufficiently characterize weak-texture targets under complex Martian backgrounds. To address these issues, an edge-guided dual-branch transformer network (EDTNet) fusing a convolutional neural network (CNN) and Transformer is proposed for automated LEC extraction. The encoder adopts a dual-branch design integrating CNN and Transformer, retaining CNN’s local feature extraction capability while leveraging Transformer’s long-range global information capture. An edge-guided semantic focusing (EGSF) module is integrated into the CNN branch to improve the extraction accuracy of degraded LEC boundaries. A fast Fourier convolution attention (FFCA) mechanism is introduced in the Transformer branch to mitigate complex Martian background interference and strengthen weak-texture target representation. A feature fusion module (FFM) is incorporated between the two branches to fuse local and global features. Extensive experiments on the self-constructed Mars layered ejecta crater (MLEC dataset) demonstrate that EDTNet achieves an mIoU of 90.35%, outperforming SOTA methods such as MobileNet, DeepLabv3, ConvNeXt, Swinformer, SegFormer, and EASSA.","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":"148491098","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

Enhancing DeepLabv3+ With Multiscale Sparse-Aware Dilated Convolution and Boundary-Interactive ASPP for River Ice Segmentation 基于多尺度稀疏感知扩展卷积和边界交互ASPP的DeepLabv3+河冰分割增强

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: 2026-02-23 DOI: 10.1109/LGRS.2026.3667024

Dingju Zhou;Kaijun Wu;Junmei Hu;Qi Sun

引用次数: 0

Multisource Remote Sensing Data Classification Based on Quadruple Agent Attention Assisted Modal Feature Perception 基于四代理注意辅助模态特征感知的多源遥感数据分类

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: 2025-12-29 DOI: 10.1109/LGRS.2025.3649409

Qingyan Wang;Ran Zhang;Junping Zhang;Yujing Wang

{"title":"Multisource Remote Sensing Data Classification Based on Quadruple Agent Attention Assisted Modal Feature Perception","authors":"Qingyan Wang;Ran Zhang;Junping Zhang;Yujing Wang","doi":"10.1109/LGRS.2025.3649409","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3649409","url":null,"abstract":"The combined application of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data in land cover classification has always faced the challenge of balancing the heterogeneity and complementarity among heterogeneous data. Therefore, this has also become a hot research topic for the application of the fusion of various remote sensing data. This letter proposes a novel quadruple agent attention fusion and modal feature perception network (QATMPNet) framework to address this. First, the proposed method employs a 2-D inverted bottleneck convolution to extract features from two kinds of heterogeneous data. Subsequently, feature mapping is applied to construct a four-channel input interaction to enhance feature representation. Finally, a quadruple agent attention-guided fusion module (QAT) is designed to integrate features, and a multimodal attention mechanism is utilized to capture multimodal data interactions. The effectiveness of the proposed method was verified through experiments on three public remote sensing datasets.","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":"148494241","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