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

Spatial-Aware Remote Sensing Image Generation From Spatial Relationship Descriptions

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-14 DOI: 10.1109/LGRS.2025.3542169

Yaxian Lei;Xiaochong Tong;Chunping Qiu;Haoshuai Song;Congzhou Guo;He Li

引用次数: 0

Enhancing Landslide Detection: A Novel LA-YOLO Model for Rainfall-Induced Shallow Landslides

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3541867

Lin Wang;Henggang Lei;Wenbin Jian;Wenjia Wang;Hao Wang;Nan Wei

{"title":"Enhancing Landslide Detection: A Novel LA-YOLO Model for Rainfall-Induced Shallow Landslides","authors":"Lin Wang;Henggang Lei;Wenbin Jian;Wenjia Wang;Hao Wang;Nan Wei","doi":"10.1109/LGRS.2025.3541867","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3541867","url":null,"abstract":"As a geological disaster widely distributed in the southern regions of China, rainfall-induced shallow landslides pose a significant threat to affected areas. Timely detection of landslides is crucial in the effective response to such disasters. However, landslide detection faces adverse impacts from various factors, such as insufficient sample data, complex model structures, and limitations in detection accuracy during the actual detection process. In this study, high-quality image samples were collected from multiple landslide disaster areas in southern China, and a rainfall-induced shallow landslide sample database was constructed in the region. Based on this, a lightweight attention-guided YOLO model (LA-YOLO) was proposed to improve the detection performance of YOLO model for rainfall-induced shallow landslides. First, CG block is introduced to enhance the C2f module, enriching the feature representation capability through multiscale feature fusion and reducing the model’s parameters and computational complexity. Second, the SimAM attention module is used to focus on the target regions, improving feature extraction effectiveness. Experimental results show that the model parameters of LA-YOLO were reduced by approximately 30%, with precision, recall, and mean average precision (mAP) on the landslide sample dataset increasing by 2.6%, 0.7%, and 2.2%, respectively. While ensuring model detection performance, the model structure was significantly optimized, achieving both lightweight and accuracy goals, confirming the model’s superiority in monitoring rainfall-induced shallow landslide disasters.","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":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621618","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

MBSSNet: A Mamba-Based Joint Semantic Segmentation Network for Optical and SAR Images

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3541895

Jie Li;Zhanhong Liu;Shujun Liu;Huajun Wang

{"title":"MBSSNet: A Mamba-Based Joint Semantic Segmentation Network for Optical and SAR Images","authors":"Jie Li;Zhanhong Liu;Shujun Liu;Huajun Wang","doi":"10.1109/LGRS.2025.3541895","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3541895","url":null,"abstract":"The utilization of both optical and synthetic aperture radar (SAR) images for joint semantic segmentation enhances the accuracy of land use classification. Recent advancements in multimodal fusion models, particularly those using self-attention mechanisms and convolutional neural networks (CNNs), have yielded significant results. However, self-attention has quadratic computational complexity, and CNN has insufficient local-global contextual modeling power. Recently, 2-D-selective-scan (SS2D) has emerged as a promising approach. It excels in modeling long-range dependencies while maintaining linear computational complexity. Based on SS2D, we propose a joint semantic segmentation network for optical and SAR images, called MBSSNet. Specifically, we introduce SS2D and design a cross-modal fusion module (CMFM) to fuse multimodal features from dual branches layer by layer, thereby enhancing the consistency of fused feature representations. In addition, during the decoding phase, we integrate contextual information from multiscale fusion features, thereby enhancing the spatial and semantic information of the fused features. Our experimental results show that our method outperforms the state-of-the-art (SOTA), and overall accuracy (OA), mean intersection over union (mIoU), and Kappa outperform other SOTA methods by 1.7%, 3.1%, and 2.2%, respectively.","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":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570562","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

Magnetic Characteristics of Deep-Seated “Panzhihua-Type” Vanadium-Titanium Magnetite Based on 3-D Aeromagnetic Inversion

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3541342

Chu Jian;Jun Li;Zhengwei Xu;Xiaolin Tian;Zhipeng Cheng;Jiayue Deng;Mujing Lan;Yue Sun

{"title":"Magnetic Characteristics of Deep-Seated “Panzhihua-Type” Vanadium-Titanium Magnetite Based on 3-D Aeromagnetic Inversion","authors":"Chu Jian;Jun Li;Zhengwei Xu;Xiaolin Tian;Zhipeng Cheng;Jiayue Deng;Mujing Lan;Yue Sun","doi":"10.1109/LGRS.2025.3541342","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3541342","url":null,"abstract":"The deep exploration potential of basic-ultrabasic rock masses associated with Panzhihua-type vanadium-titanium magnetite (VTM) deposits are closely tied to the occurrence of deep-seated rock bodies. In this study, we utilized newly acquired 1:50000 scale aeromagnetic data from the Panxi region to perform a 3-D magnetization inversion using an improved regularized focusing conjugate gradient approach to achieve high-resolution 3-D magnetic imaging of basic-ultrabasic rock masses within the “Panzhihua-type” VTM concentration zone at depths reaching 10 km. The inversion results reveal that the 3-D magnetic anomalies of strong magnetic sources correspond with the distribution of the NS fault zones in the study area. However, these anomalies are predominantly located within narrow zones between the fault zones rather than directly along the fault lines. It also suggests that during the Late Huashan period, two rift regions might have developed in the Panxi area: the Anninghe Rift and the Panzhihua Rift. The deep and large faults within these confined rift valleys likely controlled the eruption and intrusion of mantle-derived magma, facilitating the emplacement of basic-ultrabasic strong magnetic rock masses along these zones. Additionally, the local shear structures within the paleo-rift zones may have provided ample space and a relatively stable environment conducive to the formation of VTM deposits.","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":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455230","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

Adaptive Downsampling and Scale Enhanced Detection Head for Tiny Object Detection in Remote Sensing Image

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3532983

Yunzuo Zhang;Ting Liu;Jiawen Zhen;Yaoxing Kang;Yu Cheng

{"title":"Adaptive Downsampling and Scale Enhanced Detection Head for Tiny Object Detection in Remote Sensing Image","authors":"Yunzuo Zhang;Ting Liu;Jiawen Zhen;Yaoxing Kang;Yu Cheng","doi":"10.1109/LGRS.2025.3532983","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3532983","url":null,"abstract":"In recent years, the detection for tiny objects in remote sensing images has become a hot research topic. Tiny objects contain a limited number of pixels and are easily confused with the background, which leads to low detection accuracy. To the end, this letter proposes a tiny object detection method based on adaptive downsampling and scale enhanced detection head (SEDH) to improve the accuracy of detection without increasing the model parameters. First, the dynamic feature extraction module (DFEM) is proposed. The module can obtain the context information of tiny objects. Second, the adaptive downsampling module (ADM) is designed to capture local details of tiny objects. Finally, the scale enhanced detection head is constructed which improves the sensitivity to tiny objects, while reducing the number of parameters of the model. To verify the effectiveness of the proposed method, a series of experiments are conducted on the challenging AI-TOD dataset. The experimental results demonstrate that the proposed method effectively trade-offs the relationship between detection accuracy and the number of model parameters.","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":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430564","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

Augmentation and Evaluation of Geological Tabular Data: Geo-TabGAN Model and Its Applications

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3541770

Pengfei Lv;Weiying Chen;Xinyu Zou

{"title":"Augmentation and Evaluation of Geological Tabular Data: Geo-TabGAN Model and Its Applications","authors":"Pengfei Lv;Weiying Chen;Xinyu Zou","doi":"10.1109/LGRS.2025.3541770","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3541770","url":null,"abstract":"Data augmentation plays a crucial role in data-driven geoscience research by minimizing sampling costs and improving the generalization and predictive accuracy of models utilized in mineral exploration and oil and gas development. Although geoscience data are predominantly structured in tabular form, research focused on the augmentation of such structured data remains in its nascent stages. This study seeks to address two fundamental questions: 1) is it feasible to generate realistic synthetic data when only a limited amount of labeled data are available? and 2) what criteria can be established to evaluate synthetic data to ensure it contributes positively to model performance? To this end, we introduce the geological tabular data generative adversarial network (Geo-TabGAN) model and propose a comprehensive evaluation framework. Experimental results derived from core analysis data of the Bayan Obo deposit in Inner Mongolia indicate that the integration of synthetic data led to improvements exceeding 5% in the average accuracy, precision, recall, <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> score, and Matthews correlation coefficient (MCC) across three classifiers. This methodology significantly enhances the efficacy of big data analysis and predictive modeling within the geoscience domain.","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":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496485","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

Progressive Dynamic Queries Reformation-Based DETR for Remote Sensing Object Detection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3541662

Haitao Yin;He Wang;Zhuyun Zhu

{"title":"Progressive Dynamic Queries Reformation-Based DETR for Remote Sensing Object Detection","authors":"Haitao Yin;He Wang;Zhuyun Zhu","doi":"10.1109/LGRS.2025.3541662","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3541662","url":null,"abstract":"Object queries-based detection transformer (DETR) makes remarkable achievements in object detection. However, most object queries design approaches are initialized with only one input and shared among all samples, which may result in the propagation of probing errors and lacking understanding of remote sensing objects with diversified structures and complex backgrounds. To address these issues, this letter proposes a progressive dynamic queries reformation (PDQR) for DETR-based remote sensing object detection, which consists of multihierarchical dynamic object queries and progressive reformation. A group of unique object queries are dynamically weighted, which are then fed into the current stage of decoder to reform the updated object queries of previous stage. This progressive reformation can suppress error propagation from earlier stages and reduce the influences of backgrounds. Moreover, the dynamic object queries can enhance the awareness ability of fine-grained features. PDQR can be flexibly plugged into various DETRs. The experimental results on different benchmark datasets demonstrate the superiority of PDQR over several state-of-the-art DETRs. Specifically, the PDQR-based DINO achieves 95.9%, 80.2%, and 97.3% mAPs on NWPU VHR-10, DIOR, and RSOD datasets, respectively.","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":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480772","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

Multifrequency Omnibus Change Detection in Covariance Matrix PolSAR Data

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3541861

Allan A. Nielsen;Henning Skriver;Knut Conradsen

引用次数: 0

Amplitude-Preserving 3-D TV Regularization for Seismic Random Noise Attenuation

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3542040

Peng Zhang;Yaju Hao;Hongxing Li;Hua Zhang;Duowen Yin;Hanbing Ai

引用次数: 0

Reflection Coefficient Estimation of Underlying Rough Surface for Calibrated Polarimetric Measurements in Multipath Environment

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-13 DOI: 10.1109/LGRS.2025.3541671

Kuan Yang;Qianhai Wang;Xiaojian Xu

引用次数: 0