IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第3页

Low-Frequency Seismic Data Reconstruction Using Deep-Learning Refined Deconvolution 基于深度学习精细反卷积的低频地震数据重建

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3588008

Zhaoqi Gao;Weiwei Yang;Qiu Du;Lei Wang;Jinghuai Gao

{"title":"Low-Frequency Seismic Data Reconstruction Using Deep-Learning Refined Deconvolution","authors":"Zhaoqi Gao;Weiwei Yang;Qiu Du;Lei Wang;Jinghuai Gao","doi":"10.1109/LGRS.2025.3588008","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3588008","url":null,"abstract":"Low-frequency (LF) data play a key role in mitigating cycle-skipping in full waveform inversion (FWI). We propose a method to efficiently and accurately reconstruct LF seismic data for a large number of shot gathers based on multichannel deconvolution (MD) and deep learning (DL). Specifically, we first propose an MD method to predict LF data for very limited shot gathers. Then, we use a deep neural network (called “acceleration network”) to learn the relation between a shot gather and its corresponding LF data, based on the labels provided by the MD method, enabling efficient prediction for all shot gathers. Next, another deep neural network (called “improvement network”) is proposed to improve the accuracy of the LF shot gathers predicted by the “acceleration network.” To do so, several horizontal layered velocity models are generated based on the statistical distribution of well logs, and several synthetic shot gathers with and without LF are generated by solving the acoustic wave equation. Based on these synthetic shot gathers, the MD predicted LF data and the corresponding true LF data form a data pair [predicted LF, true LF] for each shot gather, and these data pairs are used to train the “improvement network.” Finally, employing a cascade of “acceleration network” and “improvement network,” we reconstruct the LF data of all shot gathers. Synthetic and field data examples verify that the proposed method exhibits superior accuracy compared to conventional MD method in LF data reconstruction.","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":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671115","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

High-Quality MIMO Sonar Imaging Using Cross Correlation Suppression 利用互相关抑制的高品质MIMO声纳成像

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3588729

Jiahao Fan;Xionghou Liu;Xin Yao;Yixin Yang

{"title":"High-Quality MIMO Sonar Imaging Using Cross Correlation Suppression","authors":"Jiahao Fan;Xionghou Liu;Xin Yao;Yixin Yang","doi":"10.1109/LGRS.2025.3588729","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3588729","url":null,"abstract":"Multiple-input–multiple-output (MIMO) sonar systems enhance imaging resolution by synthesizing a virtual array produced by orthogonal transmitted waveforms and a bank of matched filters. Up- and down-chirp pulses are widely used due to their approximate orthogonality and robustness for underwater remote sensing applications. However, the cross correlation function (CCF) terms between them always exist and thereby degrade the quality of the MIMO sonar image. In this letter, we propose a novel cross correlation suppression method for improving the image quality of MIMO sonar. The proposed method leverages the frequency-domain differences between the envelopes of autocorrelation functions (ACFs) and CCFs of up- and down-chirp pulses. After the Fourier transformed, the envelopes of ACFs are approximately to be square wave function, while CCFs are approximately to be sinc function. Therefore, CCFs’ energy is concentrated and easier to be suppressed. By setting the main components of the CCFs to zero in the frequency domain of the multibeam outputs’ envelopes, the method effectively suppresses range sidelobes (SLs) caused by cross correlation, thereby enhancing the quality of the imaging results. Through numerical simulations and lake experiments, we quantitatively analyze the performance of the proposed method and demonstrate its effectiveness in MIMO sonar imaging.","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":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671147","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

Low-Cost MLS-Based Forest Plot Mapping via Feature Graph Registration 基于特征图配准的低成本森林图映射

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3589100

Qin Ye;Yujia Jin;Junqi Luo

引用次数: 0

A Lightweight Pipeline for Crop Time-Series Parcel Classification via Self-Supervision 基于自监督的作物时序包分类轻量级管道

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3589176

Mateus Pinto da Silva;Lucas Vieira;Cleverton T. C. de Santana;Ian M. Nunes;Jefersson A. Dos Santos;Hugo N. Oliveira

{"title":"A Lightweight Pipeline for Crop Time-Series Parcel Classification via Self-Supervision","authors":"Mateus Pinto da Silva;Lucas Vieira;Cleverton T. C. de Santana;Ian M. Nunes;Jefersson A. Dos Santos;Hugo N. Oliveira","doi":"10.1109/LGRS.2025.3589176","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3589176","url":null,"abstract":"This study proposes and benchmarks a lightweight pipeline for large-scale parcel-level crop classification using time-series data. The key contributions include a scalable satellite image time-series (SITS) download strategy, a novel parcel-level crop classification dataset for the USA States of Texas and California, and a benchmark of classification techniques tested under varied data availability scenarios, highlighting the pipeline’s potential for enhancing agricultural monitoring systems. The methodology extracts basic descriptive statistics integrating with agricultural and cloud filtering, leveraging field boundaries delineation and USDA Cropland Data Layer datasets. Experimental results show that pretrained and fine-tuned models, such as SITS-BERT, outperform random forest (RF) and support vector machine (SVM) approaches, achieving an <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> score of 98.2% and overall accuracy (OA) of 99.3% on the Texas dataset for the more abundant training data scenarios. The pipeline has a projected computational speed-up of at least <inline-formula> <tex-math>$2 500times $ </tex-math></inline-formula> compared with pixel-based methods for the tested datasets. The download pipeline is available on AgriGEE.lite library in <uri>https://pypi.org/project/agrigee-lite</uri>. The full benchmark results, related code and supplementary materials are available at <uri>https://github.com/mateuspinto/light-crop-classification</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":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725176","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 Lightweight Hybrid Network for Object Detection in Remote Sensing Images Balancing Global and Local Information 一种平衡全局和局部信息的遥感图像目标检测轻量级混合网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3588788

Shuting Huang;Ge Zhang;Huanzun Zhang;Hui Xu;Guangzhen Yao;Sandong Zhu;Long Zhang;Jun Kong

{"title":"A Lightweight Hybrid Network for Object Detection in Remote Sensing Images Balancing Global and Local Information","authors":"Shuting Huang;Ge Zhang;Huanzun Zhang;Hui Xu;Guangzhen Yao;Sandong Zhu;Long Zhang;Jun Kong","doi":"10.1109/LGRS.2025.3588788","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3588788","url":null,"abstract":"In recent years, hybrid convolutional neural networks (CNNs) and Transformer-based object detection technologies have achieved remarkable success. In the field of remote sensing image detection, since remote sensing systems rely on the large-scale deployment of edge devices, detection models need to be lightweight with low parameter complexity to adapt to resource-constrained environments. However, existing lightweight models often struggle with an imbalance in extracting low-frequency global and high-frequency local information. In particular, when processing high-frequency local information (such as edges, textures, and fine structures), these models often lack in-depth analysis, leading to insufficient extraction of local features and reduced detection accuracy. To address the imbalance between low-frequency global information and high-frequency local information in lightweight remote sensing models, we propose an efficient and lightweight hybrid network detection framework, which mainly consists of the global–local balance (GLB) module and the detail-aware feature fusion (DAFF) module. The GLB module adopts dynamic weight adjustment and context-aware mechanisms to effectively aggregate high-frequency local information in the image. The DAFF module further enhances feature fusion and detail refinement, improving the model’s performance and generalization ability. Experimental results on remote sensing datasets, including RSOD, NWPU VHR-10, and LEVIR datasets, demonstrate that our proposed method achieves a well-balanced tradeoff between model size and detection accuracy, reaching state-of-the-art performance.","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":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695550","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

Collaborative Representation-Based Attention Network for Hyperspectral Anomaly Detection 基于协同表示的高光谱异常检测注意网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-11 DOI: 10.1109/LGRS.2025.3588163

Maryam Imani;Daniele Cerra

引用次数: 0

On the Detection of the Terra Nova Bay Open Polynya Dynamic Phases Using a Single C-Band SAR Image 基于单幅c波段SAR图像的特拉诺瓦湾开放冰谷动态相位检测研究

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-11 DOI: 10.1109/LGRS.2025.3588103

Giovanna Inserra;Ferdinando Nunziata;Andrea Buono;Giuseppe Aulicino;Maurizio Migliaccio

引用次数: 0

Calibration of a Radar Polarimetric Decomposition Using a Radiative Transfer Model 利用辐射传输模型标定雷达极化分解

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-11 DOI: 10.1109/LGRS.2025.3588254

Giovanni Anconitano;Lorenzo Giuliano Papale;Leila Guerriero;Mario Alberto Acuña;Nazzareno Pierdicca

{"title":"Calibration of a Radar Polarimetric Decomposition Using a Radiative Transfer Model","authors":"Giovanni Anconitano;Lorenzo Giuliano Papale;Leila Guerriero;Mario Alberto Acuña;Nazzareno Pierdicca","doi":"10.1109/LGRS.2025.3588254","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3588254","url":null,"abstract":"This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman–Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained by applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on the L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the root mean square error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in-situ data, shows a significant improvement. The calibrated GFD powers lead to an increased linear correlation (from 0.32 to 0.57), while the RMSE is reduced (from 0.096 to 0.055 m<sup>3</sup>/m<sup>3</sup>).","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":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687809","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

Moving Magnetic Target Localization Using Automatically Initialized KalmanNet Driven by Euler Deconvolution 欧拉反褶积驱动的自动初始化卡尔曼网运动磁目标定位

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-11 DOI: 10.1109/LGRS.2025.3588435

Jing Zhao;Zhen Wang;Zonghu Liu;Wenliang Cao;Yan Huang;Pei Qin;Liangguang Yue

{"title":"Moving Magnetic Target Localization Using Automatically Initialized KalmanNet Driven by Euler Deconvolution","authors":"Jing Zhao;Zhen Wang;Zonghu Liu;Wenliang Cao;Yan Huang;Pei Qin;Liangguang Yue","doi":"10.1109/LGRS.2025.3588435","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3588435","url":null,"abstract":"The task of moving magnetic target localization involves recovering hidden states from noisy, nonlinear magnetic field observations. While nonlinear filters like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are effective, they depend heavily on knowledge of underlying noise statistics. Besides, a lack of prior knowledge can lead to poor accuracy and robustness in state estimation. To address these two limitations, this letter proposes a novel filtering algorithm that automatically estimates the initial state value. An initial value estimator is created using three-axis magnetic scalar gradient data and the Euler deconvolution to quickly determine the target’s initial state. This value is then used as prior knowledge in a new neural network-aided Kalman filter called KalmanNet for state prediction and updating. Unlike traditional methods that transform nonlinear equations into linear forms, the proposed method does not require specific assumptions, preserving target characterization generality. Furthermore, this method provides a reliable initial state value while not relying on statistical noise knowledge, enhancing estimation accuracy. Simulation and field experiments show that the estimation results closely align with true values. In the field experiment, the root mean square error (RMSE) values for position, velocity, and magnetic moment vectors were reduced by 69.21%, 86.30%, and 60.55% compared to the EKF, and by 75.49%, 83.74%, and 80.45% compared to the UKF.","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":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739959","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

EfficientSAM-Assisted Network With Feature Interaction and Priori Guidance for Remote Sensing Change Detection 基于特征交互和先验制导的高效sam辅助网络遥感变化检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-10 DOI: 10.1109/LGRS.2025.3587852

Qinghao Peng;Kun Liu;Benli Zou;Meishu Li

{"title":"EfficientSAM-Assisted Network With Feature Interaction and Priori Guidance for Remote Sensing Change Detection","authors":"Qinghao Peng;Kun Liu;Benli Zou;Meishu Li","doi":"10.1109/LGRS.2025.3587852","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3587852","url":null,"abstract":"Change detection is one of the important methods for Earth observation, and with the rapid development of deep learning, more effective methods are provided for the change detection task. However, many methods do not pay attention to the correlation between bitemporal remote sensing images and cannot effectively extract change information, which leads to the problems of poor recognition of pseudochanges and the appearance of internal voids with inadequate expression of change features. In this letter, we propose an EfficientSAM-assisted feature interaction and a priori guidance network (EfficientSAM-FG) to address these issues. We enhance feature extraction and generalization capabilities by fine-tuning EfficientSAM. We introduce a multiscale feature interaction (MFI) module to learn spatial information correlations between diachronic features. We design a change-guided module (CGM) in the feature fusion stage to guide multiscale feature fusion with prior knowledge. Finally, we use a space attention module (SAT) to obtain a location attention map, enhancing change region features and suppressing background region features. To verify the effectiveness of our method, we compare it with other methods on two datasets, LEVIR-CD and WHU-CD, and the experimental results prove that EfficientSAM-FG can achieve better results, with <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> of 92.15% and IoU of 85.41% on LEVIR-CD and 94.50% and 89.42% on WHU-CD.","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":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750950","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