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

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

Methane Column Estimation Using PRISMA Hyperspectral Data and Comparison With Other Earth Observation Products

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.2025.3539870

Daniele Settembre;Davide De Santis;Giovanni Schiavon;Fabio Del Frate

引用次数: 0

A Postdetection Framework With Optimal Transport for Multiclass Object Change 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.2025.3541828

Tian Lu;Zi Wang;Junfang Wang;Xiguan Li;Zhang Li

{"title":"A Postdetection Framework With Optimal Transport for Multiclass Object Change Detection","authors":"Tian Lu;Zi Wang;Junfang Wang;Xiguan Li;Zhang Li","doi":"10.1109/LGRS.2025.3541828","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3541828","url":null,"abstract":"Current research on change detection has made significant progress on large-scale landscapes and buildings. However, there is a lack of exploration into the status changes of multiclass and time-sensitive objects across different temporal of remote sensing images (RSIs). To bridge this gap, we first introduce a task termed multiclass object change detection (MCOCD) and then construct a dedicated dataset dubbed aircraft change detection (ACD). Furthermore, we propose a postdetection framework to address this task. In the framework, we first feed bitemporal RSIs into an object detector to obtain the bounding boxes (BBOXs) of predefined classes. Subsequently, we utilize the intersection over union (IoU)-based distance to ascertain changes. Nervelessly, due to the dense arrangement of objects in RSIs, directly using IoU-based distance to determine changes results in one-to-many or many-to-one matching problems. To address this issue, we propose an optimal transport (OT) module to compute the global optimal matching of distance matrices, which are bidirectional augmented with dustbin nodes. Finally, the detected objects that are matched with the dustbin nodes being regarded as the changed ones. Extensive experiments demonstrate the effectiveness of our 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":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521471","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

Small-Scale Martian Crater Detection by Deep Learning With Enhanced Capture of Features Information and Long-Range Dependencies

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-11 DOI: 10.1109/LGRS.2025.3539947

Zhichao Yu;Simon Fong;Richard Charles Millham

{"title":"Small-Scale Martian Crater Detection by Deep Learning With Enhanced Capture of Features Information and Long-Range Dependencies","authors":"Zhichao Yu;Simon Fong;Richard Charles Millham","doi":"10.1109/LGRS.2025.3539947","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3539947","url":null,"abstract":"Currently, with the rapid advancement of aerospace technology, scientists are increasingly capable of exploring other planets, which has brought greater attention to the challenge of crater detection, particularly the detection of smaller craters. This letter presents a novel single-stage target detector based on YOLOv9, aimed at improving the detection of craters, especially small ones. First, we propose a novel feature extraction module that enhances the detection capabilities of the network. By integrating deformable convolution into the original feature extraction module, we improve the capability of convolutional neural network (CNN) to catch long-range dependencies and spatial relationships, thereby enhancing the detection accuracy for craters of various sizes. Second, we introduce a new pooling structure called averaged spatial pyramid pooling (ASPP). This structure uses a parallel configuration of average and maximum pooling techniques to enrich the overall feature extraction process, thereby improving the detection capability for small craters. To confirm the efficacy of our proposed approach, we performed comprehensive experiments using a large public Mars crater dataset. The results indicate that our approach significantly surpasses most current mainstream one-stage object detection algorithms in both precision and recall.","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":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553454","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

Magnetotelluric Inversion Based on Double-Layer Convolutional Neural Network

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-10 DOI: 10.1109/LGRS.2025.3540113

Han Lin;Jianfeng Jin;Jianliang Zhuo;ChangMing Shen;Qing Huo Liu

引用次数: 0