IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第2页

Nonlocal Affinity-Based Robust Interference-Resistant Model for Infrared Small Target Detection 基于非局部亲和力的红外小目标鲁棒抗干扰模型

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-29 DOI: 10.1109/LGRS.2025.3565538

Jiakun Deng;Xingye Cui;Kexuan Li;Junsong Hu;Chang Long;Yizhuo Yin;Tian Pu;Zhenming Peng

{"title":"Nonlocal Affinity-Based Robust Interference-Resistant Model for Infrared Small Target Detection","authors":"Jiakun Deng;Xingye Cui;Kexuan Li;Junsong Hu;Chang Long;Yizhuo Yin;Tian Pu;Zhenming Peng","doi":"10.1109/LGRS.2025.3565538","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565538","url":null,"abstract":"Infrared small target detection (ISTD) is a fundamental component of infrared search and tracking (IRST) systems. The low-rank sparse decomposition (LRSD) method has become the mainstream of ISTD due to its broad applicability across various scenarios. However, certain sparse interferences in complex backgrounds may limit the effectiveness of these methods. To solve this problem, we propose a nonlocal affinity-based robust interference-resistant model (NARIRM) for ISTD. The model leverages the concept of affinity, which denotes the relationship between pixel regions, assuming that interference has stronger affinity with its neighbors than the target. The affinity values are achieved by reformulating an infrared image as the linear combination of foreground and background and using sparse decomposition results as constraints. A suppressor is then derived to reduce the impact of sparse interference by the affinity values. Experimental evaluation on public datasets demonstrates the proposed method outperforms several state-of-the-art techniques. The code is available at <uri>https://github.com/djk1997-jk/NARIRM</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":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925148","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

Re-Evaluation of Lunar Regolith Thickness Using Relative Microwave Brightness Temperature of Chang’E-2 Microwave Radiometer

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-28 DOI: 10.1109/LGRS.2025.3564908

Meng Lv;Qianyun Mao;Wenchao Zheng;Guoping Hu

{"title":"Re-Evaluation of Lunar Regolith Thickness Using Relative Microwave Brightness Temperature of Chang’E-2 Microwave Radiometer","authors":"Meng Lv;Qianyun Mao;Wenchao Zheng;Guoping Hu","doi":"10.1109/LGRS.2025.3564908","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564908","url":null,"abstract":"The exploration of the Moon has never ceased. One of the most significant challenges is determining the thickness of the lunar regolith. This letter employs the relative microwave brightness temperature (TB) to invert the thickness of the lunar regolith. A multilayer parallel stratified model serves as the forward model. In the inversion process, the simulated microwave TB is derived by calculating the sum of the TB contributions from each layer. Based on the forward model, the areas where the simulated TB is sensitive to lunar regolith thickness can be identified. Subsequently, the simulated TB is compared with the measured TB by the Chang’E-2 Microwave Radiometer (MRM) at 3 GHz at midnight (24:00) of the lunar local time. The discrepancy between the observed and modeled TB at a specified location, such as the Apollo 12 landing site (A12), is regarded as a correction for the simulated TBs at other locations with the same latitude. Ultimately, the thickness of the regolith is inverted according to the corrected simulated TB. This letter compares the inverted result with the regolith thickness obtained by DEM data. It is found that regions where the model inverted results closely align with the DEM data tend to have higher FeO/TiO2 content. The uncertainty of the inversion is also discussed, which indicates that the method presented in this letter is feasible.","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":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943850","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

Toward High-Confidence Homogeneous Features: Partial Neighborhood Ratio Based Difference Image for SAR Change Detection 面向高置信度均匀特征：基于局部邻域比的SAR变化检测差分图像

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-28 DOI: 10.1109/LGRS.2025.3564600

Bin Cui;Yao Peng;Huarong Jia;Shanchuan Guo;Peijun Du

{"title":"Toward High-Confidence Homogeneous Features: Partial Neighborhood Ratio Based Difference Image for SAR Change Detection","authors":"Bin Cui;Yao Peng;Huarong Jia;Shanchuan Guo;Peijun Du","doi":"10.1109/LGRS.2025.3564600","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564600","url":null,"abstract":"The inherent speckle noise in synthetic aperture radar (SAR) images limits the accuracy of SAR image change detection. As a crucial step in unsupervised change detection, existing difference map generation methods primarily utilize neighborhood information to counteract the interference caused by speckle noise. However, pixels within the neighborhood can themselves be affected by heterogeneous pixels and noise. Therefore, this letter proposes a difference map generation method, partial neighborhood ratio (PNR), which relies on high-confidence homogeneous pixels within the neighborhood for difference calculation. Specifically, under the assumption that the local neighborhood of SAR images follows a normal distribution, we develop a method for selecting high-confidence homogeneous pixels. This method quantifies interneighborhood dissimilarity by leveraging the statistical features of predominantly homogeneous pixel clusters within an adaptive framework, thereby reducing the impact of noise and enhancing the accuracy of difference expression. Experimental results demonstrate the superior performance of the proposed PNR. The change detection results, obtained by applying both manual trial-and-error and dual-domain network (DDNet) on three SAR datasets, have validated the effectiveness of the proposed algorithm.","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":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949258","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

Dual-Branch Retrieval Network for Satellite Cloud Image Classification Based on Multilevel Semantic Information 基于多级语义信息的卫星云图分类双分支检索网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-28 DOI: 10.1109/LGRS.2025.3564728

Jiezhi Lv;Nan Wu;Wei Jin;Randi Fu

{"title":"Dual-Branch Retrieval Network for Satellite Cloud Image Classification Based on Multilevel Semantic Information","authors":"Jiezhi Lv;Nan Wu;Wei Jin;Randi Fu","doi":"10.1109/LGRS.2025.3564728","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564728","url":null,"abstract":"The weather system has a profound impact on human activities. Conducting research on satellite cloud image classification can provide critical parameters for weather forecasting, climate analysis, and severe weather detection. However, conventional satellite cloud image classification methods typically neglect higher level semantic constraints and rarely incorporate decision-level adaptive calibration, resulting in confusion among visually similar categories and restricting interpretable, content-based inference. Here, we propose a dual-branch retrieval network with multilevel semantic information (DBR-MSI) to address these gaps. DBR-MSI jointly optimizes high-level semantics (e.g., broad meteorological and surface categories) and low-level semantics (e.g., specific cloud or surface attributes), and we explicitly highlight critical semantic content via a gradient-based attention sharing module. Moreover, a retrieval-based inference approach driven by high-level semantic guidance supports interpretable content reasoning and adaptive decision calibration, which in turn allows the proposed method to deliver enhanced robustness and efficient integration of additional data. Experimental results on two satellite cloud image datasets confirm that DBR-MSI exhibits stronger interpretability and achieves overall accuracy (OA) gains of 1.06% and 0.39% over the best competing methods.","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":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931316","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 Crop Yield Estimation Through Iterative Querying and Bayesian-Optimized Gated Networks 利用迭代查询和贝叶斯优化门控网络提高作物产量估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-25 DOI: 10.1109/LGRS.2025.3564415

Benjamin K. Osibo;Tinghuai Ma;Kristina Darbinian;Bright Bediako-Kyeremeh;Lorenzo Mamelona;Jianxin Liu;Stephen Osei-Appiah

{"title":"Enhancing Crop Yield Estimation Through Iterative Querying and Bayesian-Optimized Gated Networks","authors":"Benjamin K. Osibo;Tinghuai Ma;Kristina Darbinian;Bright Bediako-Kyeremeh;Lorenzo Mamelona;Jianxin Liu;Stephen Osei-Appiah","doi":"10.1109/LGRS.2025.3564415","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564415","url":null,"abstract":"The accurate prediction of crop yield is essential not only for sustainable agriculture but also for ensuring global food security. In recent times, deep learning (DL) techniques have made significant strides in improving prediction accuracy by leveraging complex and advanced architectures. However, despite these advancements, the existing methods often struggle in modeling temporal dependencies efficiently, especially when dealing with limited data (a common challenge in crop yield prediction). To address this, an innovative iterative querying (IQ) strategy based on the principles of active learning (AL) to enhance model performance has been proposed. The aim of the IQ strategy is to maximize performance by introducing the model to a batch of uncertain instances in each iteration. The overall prediction framework consists of two key components: first, a Bayesian-optimized gated recurrent unit (GRU) method to capture the complex temporal relationships between crop variables and target yield; and second, the novel IQ strategy, which utilizes an uncertainty-driven query mechanism to refine predictions by focusing on the most challenging and uncertain data points. A comprehensive multisource data, comprising remotely sensed variables, climatic, soil, and corresponding crop yield values from the US Corn Belt region, are used to train and evaluate the proposed IQ-GRU method. Experimental results demonstrate the effectiveness of the proposed IQ-GRU framework in improving yield estimation for both in-season and end-of-season predictions over conventional methods.","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":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949259","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

Dynamic Spectral Similarity Method (DSSM)—A Novel Method for Automated Identification of Objects in Hyperspectral Imagery 动态光谱相似度法（DSSM）——一种新的高光谱图像目标自动识别方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-25 DOI: 10.1109/LGRS.2025.3564386

Harsha Chandra;Rama Rao Nidamanuri

{"title":"Dynamic Spectral Similarity Method (DSSM)—A Novel Method for Automated Identification of Objects in Hyperspectral Imagery","authors":"Harsha Chandra;Rama Rao Nidamanuri","doi":"10.1109/LGRS.2025.3564386","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564386","url":null,"abstract":"Automatic identification of object of interest in a hyperspectral imagery is promising for remote sensing applications. Spectral knowledge transfer enables autonomous comparison of reference and imagery spectra for expert-independent analysis. Knowledge-transfer-based analysis involves comparing image spectra to the reference spectra (spectral libraries) using spectral similarity metrics. However, the reference spectral databases and the imagery acquired by different sensors differ in spectral resolution and bandwidths, limiting the direct comparison of the spectra. Thus, prerequisite process of spectral resampling is required before the analysis. We propose a new method “dynamic spectral similarity method (DSSM)” that quantitatively compares spectra from sensors having different spectral resolutions. DSSM geometrically aligns two nonlinear spectra and computes an optimal alignment cost through a time-warping process in a dynamic feature space. We demonstrated the potential of DSSM by comparing spectra of diverse landscape elements obtained from various sources (satellites, airborne, spectral libraries) against reference databases. Furthermore, the proposed method is compared with spectral matching methods [spectral angle mapper (SAM), spectral information divergence2 (SID), normalized spectral similarity score (NS3)] after a spectral alignment process using a Gaussian diffusion model. The results are promising, offering 80%–90% matching accuracy in all the scenarios. DSSM enables seamless comparison of images with varying spectral characteristics, allowing selective and automatic object identification.","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":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073037","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

Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes 用于高光谱图像场景波段选择的Dropout混凝土自编码器

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-25 DOI: 10.1109/LGRS.2025.3564478

Lei Xu;Mete Ahishali;Moncef Gabbouj

{"title":"Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes","authors":"Lei Xu;Mete Ahishali;Moncef Gabbouj","doi":"10.1109/LGRS.2025.3564478","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564478","url":null,"abstract":"Deep learning-based informative band selection methods on hyperspectral images (HSIs) have recently gained intense attention to eliminate spectral correlation and redundancies. However, existing deep learning-based methods either need additional postprocessing strategies to select the descriptive bands or optimize the model indirectly due to the parameterization inability of discrete variables for the selection procedure. To overcome these limitations, this work proposes a novel end-to-end network for informative band selection. The proposed network, named Dropout concrete autoencoder (CAE), is inspired by advances in the CAE and Dropout feature ranking (Dropout FR) strategy. Unlike traditional deep learning-based methods; the Dropout CAE is trained directly given the required band subset, eliminating the need for further postprocessing. The experimental results in four HSI scenes show that the Dropout CAE achieves substantial and effective performance levels that outperform competing methods. The code is available at <uri>https://github.com/LeiXuAI/Hyperspectral</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":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073097","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

YOLO-G3CF: Gaussian Contrastive Cross-Channel Fusion for Multimodal Object Detection YOLO-G3CF：用于多模态目标检测的高斯对比跨通道融合

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-24 DOI: 10.1109/LGRS.2025.3564181

Abdelbadie Belmouhcine;Minh-Tan Pham;Sébastien Lefèvre

{"title":"YOLO-G3CF: Gaussian Contrastive Cross-Channel Fusion for Multimodal Object Detection","authors":"Abdelbadie Belmouhcine;Minh-Tan Pham;Sébastien Lefèvre","doi":"10.1109/LGRS.2025.3564181","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564181","url":null,"abstract":"Object detection is a crucial task in both computer vision and remote sensing. The performance of object detectors can vary across different modalities depending on lighting and weather conditions. To address these challenges, we propose a fusion module based on contrastive learning and Gaussian cross-channel attention, called Gaussian contrastive cross-channel fusion (G3CF). We integrate this module into a dual-you only look once (YOLO) architecture, forming YOLO-G3CF. The contrastive loss enforces similarity between the features sent to the detection head from both modality branches, as they should lead to the same detections. The Gaussian attention mechanism enables the model to fuse features in a higher dimensional space, enhancing discriminative power. Extensive experiments on VEDAI, GeoImageNet, VTUAV-det, and FLIR demonstrate that G3CF improves detection performance, achieving a mAP increase of up to 6.64% over the best single-modality baselines and outperforming prior multimodal fusion methods. Regarding model complexity, our fusion method operates at a late stage, increasing the computational cost of single-modality YOLO by approximately 150% in terms of giga floating-point operations per second (GFLOP). For instance, YOLOv8 requires 52.84 GFLOPs, whereas YOLOv8-G3CF, due to its dual architecture and three G3CF modules, increases this to 131.22 GFLOPs. However, a single G3CF module requires only ~15 GFLOPs. Despite this overhead, our approach remains computationally less expensive than transformer-based models, e.g., ICAFusion requires 284.80 GFLOPs. Moreover, the proposed method still operates in real-time, achieving ~19 FPS on an NVIDIA RTX 2080. The code is available at <uri>https://github.com/abelmouhcine/YOLO-G3CF</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":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913521","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

BTCDNet: Bayesian Tile Attention Network for Hyperspectral Image Change Detection BTCDNet：用于高光谱图像变化检测的贝叶斯块关注网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-24 DOI: 10.1109/LGRS.2025.3563897

Junshen Luo;Jiahe Li;Xinlin Chu;Sai Yang;Lingjun Tao;Qian Shi

引用次数: 0

LH-UNet: A Lighted Histoformer-Encoded U-Net for Sea Ice Recognition With High-Resolution Remote Sensing Images h- unet：用于高分辨率遥感图像海冰识别的light Histoformer-Encoded U-Net

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-23 DOI: 10.1109/LGRS.2025.3563727

Zuomin Wang;Ying Li;Jiazhu Wang;Bingxin Liu

{"title":"LH-UNet: A Lighted Histoformer-Encoded U-Net for Sea Ice Recognition With High-Resolution Remote Sensing Images","authors":"Zuomin Wang;Ying Li;Jiazhu Wang;Bingxin Liu","doi":"10.1109/LGRS.2025.3563727","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3563727","url":null,"abstract":"The background information is complex for sea ice monitoring when using high-resolution remote sensing images, which may lead to a certain degree of difficulty in extracting sea ice information. Lighted histoformer-encoded u-shaped convolutional network (LH-Unet), a semantic segmentation neural network for sea ice fine recognition is proposed in this study. Initially, a histogram transformer block (HTB) in the histoformer model was integrated into the encoder to enhance the accuracy of sea ice recognition. Additionally, a ghost convolution enhanced by a triple attention block (GTB) was introduced, significantly reducing the number of parameters and computational load while also improving accuracy. Furthermore, the mean intersection over union (mIoU) of the LH-UNet network proposed in this study was 96.25%, and the number of parameters of the mentioned architecture is less than 1 M. Notably, LH-UNet surpasses the performance of prominent deep learning methods, including U-Net, PSPNet, HRNet, and SegFormer. The results suggest a reliable technical support for sea ice identification basing on high-resolution remote sensing. Moreover, this study provides a possibility for the monitoring and early warning of sea ice distribution.","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":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925146","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