IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第9页

Three-Dimensional Characterization of Pan-Antarctic Ice Shelf Fracture: An Integrated Deep Learning and Hydrological Analysis Framework 泛南极冰架断裂的三维表征：综合深度学习和水文分析框架

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3595934

Qian Li;Zemin Wang;Jiachun An;Baojun Zhang

{"title":"Three-Dimensional Characterization of Pan-Antarctic Ice Shelf Fracture: An Integrated Deep Learning and Hydrological Analysis Framework","authors":"Qian Li;Zemin Wang;Jiachun An;Baojun Zhang","doi":"10.1109/LGRS.2025.3595934","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3595934","url":null,"abstract":"Fractures represent vulnerable discontinuities formed under stress conditions, with their 3-D morphological parameters serving as pivotal indicators for assessing ice shelf dynamic stability. The current fracture monitoring system primarily focuses on 2-D feature analysis, and there is insufficient 3-D systematic monitoring of vertical extension processes. Based on the reference elevation model of Antarctica (REMA) DEM data, this study integrates deep learning semantic segmentation with hydrological terrain analysis methods to construct a framework for extracting fracture depth information. For the first time, a comprehensive dataset of fracture depths across the Antarctic ice shelves is created, and based on this dataset, the 3-D extent of ice shelf damage is quantified and evaluated. The study shows that the average depth of fractures in ice shelves is 8.17 m, with differences between ice shelves reaching up to ten times. Notable spatial variations in fracture depth are also observed within ice shelves. The depth distribution of fractures exhibits significant spatial coupling with the stretching stress field of the ice shelf. The 3-D morphological parameters of the ice shelf (average depth, area density, volume density, and penetration rate) exhibit significant spatial heterogeneity. This study fills the gap in the vertical dimension of fracture 3-D modeling, providing essential data support for ice shelf stability research.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861297","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 Passive Synthetic Aperture Localization Method Based on Sparse Sampling Reconstruction 基于稀疏采样重构的被动合成孔径定位方法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3596123

Jiayu Sun;Hao Huan;Ran Tao;Yue Wang

{"title":"A Passive Synthetic Aperture Localization Method Based on Sparse Sampling Reconstruction","authors":"Jiayu Sun;Hao Huan;Ran Tao;Yue Wang","doi":"10.1109/LGRS.2025.3596123","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3596123","url":null,"abstract":"In passive localization, the synthetic aperture positioning (SAP) method can achieve high precision and high-resolution positioning. However, existing research neglects the issue of target adaptability. For radar emitter targets, receivers can only periodically capture signals when the emitter’s beam scans toward the receiving antenna, resulting in spectral aliasing of the received signals. This leads to multiple false targets in localization images and reduced accuracy. This study employs the fractional Fourier transform (FrFT) integrated with compressed sensing for continuous signal reconstruction, aiming to eliminate spurious targets and enhance positioning accuracy. Initially, spectral aliasing is suppressed through FrFT, capitalizing on the approximately linear frequency-modulated (LFM) characteristics inherent in Doppler signals. Subsequently, a continuous signal is reconstructed using compressed sensing with FrFT basis vectors forming the sensing matrix. Finally, the SAP method is implemented to achieve precise positioning. The effectiveness of the proposed method has been validated through simulations and uncrewed aerial vehicle (UAV) experiments, demonstrating that it significantly enhances the adaptability of SAP methods to radar emitter targets.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831942","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 LoGI Phase Unwrapping Algorithm for Ku-SFCW Radar Interferometry Measurement of Wave Height Ku-SFCW雷达干涉测量波高的LoGI相位展开算法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3595866

Limin Zhai;Yifan Gong;Yan Jia;Yongqing Liu;Xiangkun Zhang

{"title":"A LoGI Phase Unwrapping Algorithm for Ku-SFCW Radar Interferometry Measurement of Wave Height","authors":"Limin Zhai;Yifan Gong;Yan Jia;Yongqing Liu;Xiangkun Zhang","doi":"10.1109/LGRS.2025.3595866","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3595866","url":null,"abstract":"The monitoring of wave height is of great significance in multiple fields such as marine safety, ecological protection, and climate change response. The ground based (GB) has the advantage of high range resolution. The built TI millimeter-wave (mmWave) radar system has achieved 1-D time-series wave height measurement, and the consistency between simulation and experimental results demonstrates the capacity of wave height measurement. Another independently built Ku-band stepped frequency continuous-wave (Ku-SFCW) radar system has achieved 2-D wave height time-series interferometry measurement. Given the traditional fast Fourier transform (FFT) algorithm, combined transport of intensity equation (TIE) with a Gaussian operator, a Laplacian of Gaussian (LoG) phase unwrapping (PU) expression was derived. Then, the 2-D LoG iteration (LoGI) PU algorithm was proposed by using an iterative strategy. The experimental results show that the LoGI algorithm is superior to the FFT algorithm and prove the effectiveness of Ku-SFCW radar system interferometry measurement of wave height. The experimental results proved the superiority of the 2-D LoGI PU algorithm in inverting the Kelvin wake wave height of the ship measured by the Ku-SFCW radar system under laboratory conditions.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880544","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

𝒩𝒮𝑒𝑔𝓂𝑒𝑛𝑡: Label-Specific Deformations for Remote Sensing Image Segmentation 𝒮𝑒𝑔𝓂𝑒𝑛𝑡：用于遥感图像分割的标签特定变形

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3595851

Yechan Kim;Dongho Yoon;SooYeon Kim;Moongu Jeon

引用次数: 0

An Omega-K 3-D SAR Imaging Algorithm Based on Fractional-Order OAM 基于分数阶OAM的Omega-K三维SAR成像算法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3596158

Yu Liu;Yongxing Du;Baoshan Li;Chenlu Li;Ling Qin;Minchao Li

{"title":"An Omega-K 3-D SAR Imaging Algorithm Based on Fractional-Order OAM","authors":"Yu Liu;Yongxing Du;Baoshan Li;Chenlu Li;Ling Qin;Minchao Li","doi":"10.1109/LGRS.2025.3596158","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3596158","url":null,"abstract":"Electromagnetic vortex radar, with its unique capacity to carry orbital angular momentum (OAM) and its spiral phase wavefront, offers a groundbreaking approach to achieving super-resolution radar imaging. This letter, grounded in the theory of OAM and plane electromagnetic wave synthetic aperture radar (SAR) imaging, delves into and analyzes SAR imaging technology based on eddy electromagnetic waves. By integrating the characteristics of vortex electromagnetic waves with an orthometric downward electromagnetic vortex SAR imaging model, we have designed a corresponding imaging model and derived the imaging echo formula. Furthermore, we propose a novel 3-D Omega-K imaging algorithm for multiscatter point targets, based on fractional OAM. This 3-D Omega-K imaging algorithm first processes the echo signals of a fixed OAM mode across the entire sampling time to obtain the target’s range and along-track information, maintaining high-resolution in the along-track direction of 2-D electromagnetic vortex SAR. Then, by processing the 2-D data consisting of vortex electromagnetic echoes of various modes transmitted and received at a specific moment, it acquires the target’s azimuth information. Finally, through geometric relationships, it derives the target’s elevation information, accomplishing the 3-D reconstruction of the target. The experimental simulations validate the algorithm’s effectiveness, with successful 3-D imaging of point targets.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914221","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

Mamba-UDA: Mamba Unsupervised Domain Adaptation for SAR Ship Detection Mamba- uda：用于SAR船舶探测的Mamba无监督域自适应

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3595843

Hong Tu;Wei Wang;Yue Guo;Shiqi Chen

{"title":"Mamba-UDA: Mamba Unsupervised Domain Adaptation for SAR Ship Detection","authors":"Hong Tu;Wei Wang;Yue Guo;Shiqi Chen","doi":"10.1109/LGRS.2025.3595843","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3595843","url":null,"abstract":"The existing synthetic aperture radar (SAR) ship detectors perform well on data with consistent distributions but degrade significantly when faced with domain shifts and the absence of labeled data. Moreover, the traditional convolutional neural networks (CNNs) struggle with global feature extraction due to the local receptive fields, while transformer approaches struggle with computational efficiency when extracting global features from complex SAR images. Designing an effective cross-domain SAR ship detector that can handle unlabeled data with domain shifts remains a challenge. In this letter, we propose a novel Mamba-based unsupervised domain adaptation (UDA) SAR ship detection model integrated with pseudolabels optimization strategy. First, we propose the domain adaptive state-space module (DASSM) to construct the Mamba mean teacher (MMT) framework for the first time, enhancing the capture of both global and local SAR image features at a linear time complexity and facilitating domain-invariant feature learning. To enhance the quality of pseudolabels, we design the adaptive pseudolabel optimizer (APLO) module with wise-IoU (WIoU) and dynamic dual-threshold pseudolabel selector (DDPLS). The WIoU is utilized to improve the generation of pseudolabels, while DDPLS is further employed to categorize and optimize pseudolabels. Extensive experiments on public datasets illustrate the effectiveness and superiority of the proposed method for cross-domain detection of unlabeled SAR data.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868192","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

PLDF-S3: Pseudo-Label-Driven Framework for Offshore to Inshore Unsupervised SAR Image Ship Segmentation 伪标签驱动的近海到近海无监督SAR图像船舶分割框架

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3595937

Wentao Li;Xinyu Wang;Haixia Xu;Liming Yuan;Furong Shi;Xianbin Wen;Jiao Liu

{"title":"PLDF-S3: Pseudo-Label-Driven Framework for Offshore to Inshore Unsupervised SAR Image Ship Segmentation","authors":"Wentao Li;Xinyu Wang;Haixia Xu;Liming Yuan;Furong Shi;Xianbin Wen;Jiao Liu","doi":"10.1109/LGRS.2025.3595937","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3595937","url":null,"abstract":"Recently, unsupervised ship segmentation methods for synthetic aperture radar (SAR) images have achieved promising results in offshore scenes. However, these methods generate a large number of false alarms in inshore scenes. To address this issue, we propose the pseudo-label-driven framework for offshore to inshore SAR image ship segmentation (PLDF-S3), which leverages ship segmentation results from offshore scenes to assist inshore ship segmentation. In particular, to account for the anisotropy of ships, which are characterized by a dominant long-axis direction, we design a directional feature enhancement module (DFEM) in PLDF-S3 to extract ship features with varying orientations. Additionally, due to the diverse size variations of ships in SAR images, we propose a hierarchical context enhancement module (HCEM) to capture ship features at different scales. Experimental results show that the proposed unsupervised PLDF-S3 achieves comparable segmentation performance than several supervised methods under challenging inshore scenarios.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914290","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

An Improved DeepLabV3+ Algorithm for Identifying Key Deformation Areas in InSAR Images InSAR图像关键变形区域识别的改进DeepLabV3+算法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3595946

Yue Zhang;Fuyang Ke;Zixuan Zhang;Yiying Sun;Atta Ur Rahman;Yule Feng;Yong Wang;Chenghua Xu

{"title":"An Improved DeepLabV3+ Algorithm for Identifying Key Deformation Areas in InSAR Images","authors":"Yue Zhang;Fuyang Ke;Zixuan Zhang;Yiying Sun;Atta Ur Rahman;Yule Feng;Yong Wang;Chenghua Xu","doi":"10.1109/LGRS.2025.3595946","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3595946","url":null,"abstract":"Conventional surface deformation monitoring predominantly depends on SAR and interferometric imagery while neglecting the comprehensive analysis of InSAR-processed deformation results, consequently limiting the precise detection of critical deformation zones in the derived data. In view of the difficulties in interpretation caused by the RGB multicolor coding of the interferometric synthetic aperture radar (InSAR) deformation rate map and the problems of low efficiency and poor accuracy of the existing automatic recognition methods, this study proposed an improved DeepLabV3+ architecture, integrated with MobileNetV2 backbone network, self-attention module, and multilevel self-attention feature fusion mechanism, to improve the accuracy and efficiency of automatic deformation detection. This study takes the main urban area of Daqing city, Heilongjiang province as the study area, and uses short-baseline InSAR technology to obtain surface deformation data and make dataset. Through ablation experiment and comparative analysis, the improved model has achieved significant improvement in indicators, such as recall (increased by 5.38%), mean intersection over union (mIoU, increased by 3.34%), pixel accuracy (PA, increased by 1.40%), and reasoning speed (shortened by 47 ms) compared with other mainstream semantic segmentation models, which can effectively identify the key surface displacement areas in InSAR images and provide reliable technical support for geological disaster prevention and control.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867544","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

Color-Robust Sea Ice Change Detection 彩色鲁棒海冰变化检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3595954

Wenjun Hong;Zhanchao Huang;Yongke Yang;Junchao Cai;Weiwang Guan;Jiajun Zhou;Luping You;Hua Su

{"title":"Color-Robust Sea Ice Change Detection","authors":"Wenjun Hong;Zhanchao Huang;Yongke Yang;Junchao Cai;Weiwang Guan;Jiajun Zhou;Luping You;Hua Su","doi":"10.1109/LGRS.2025.3595954","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3595954","url":null,"abstract":"Sea ice change detection is vital for understanding climate dynamics and ensuring maritime safety. Existing deep learning methods often struggle with the significant impact of color variations in satellite imagery, which can lead to inaccurate detection results. Moreover, the scarcity of labeled sea ice change data limits the ability of models to generalize across diverse scenarios. To address these challenges, we propose SICNet, a sea ice change detection model with enhanced color robustness and data efficiency. A wavelet-guided color-robust fusion (WCF) module is introduced to reduce low-frequency color discrepancies while preserving high-frequency edge details. In addition, a novel change-sensitive CutMix (CSC) strategy is used to augment training samples by focusing on regions with moderate changes, effectively increasing data diversity. Experiments conducted on our constructed sea ice change dataset demonstrate that SICNet achieves superior performance and robustness under varying environmental and lighting conditions. The source code of SICNet is available at <uri>https://github.com/viking-hong/SICNet.git</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831941","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

SiamSCT: Spatial-Channel Saliency and Temporal Fusion Network for Real-Time Aerial Tracking SiamSCT：用于实时空中跟踪的空间通道显著性和时间融合网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-05 DOI: 10.1109/LGRS.2025.3596119

Bo Wang;Chenglong Liu;Qiqi Chen;Jinghong Liu

{"title":"SiamSCT: Spatial-Channel Saliency and Temporal Fusion Network for Real-Time Aerial Tracking","authors":"Bo Wang;Chenglong Liu;Qiqi Chen;Jinghong Liu","doi":"10.1109/LGRS.2025.3596119","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3596119","url":null,"abstract":"Visual tracking on uncrewed aerial vehicle (UAV) platforms is a fundamental and crucial visual task. Compared to conventional tracking tasks, aerial tracking faces specific challenging scenarios due to its unique perspective. Although existing aerial trackers have demonstrated promising performance, they remain limited in capturing spatial-channel saliency across branches and effectively utilizing historical information. To address these issues, this letter proposes a spatial-channel saliency and temporal fusion network (SiamSCT), which aims to enhance feature representation for efficient and accurate aerial tracking. Specifically, SiamSCT introduces a weight-shared spatial saliency block (SSB) to strengthen the spatial feature representation across the tracking network’s branches. In addition, a light channel aware module (CAM) is designed to facilitate deep feature interaction across branches at the channel level, further enhancing feature discriminability. Finally, using a historical similarity response fusion strategy, SiamSCT achieves more stable and reliable tracking responses, effectively tackling complex aerial scenarios. Extensive experiments on several authoritative aerial tracking datasets demonstrate that SiamSCT outperforms state-of-the-art (SOTA) trackers. Furthermore, SiamSCT achieves a tracking speed of 133 frames/s on NVIDIA RTX 3060Ti, proving its excellent performance in real time.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853483","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