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

Toward Faster and Accurate Detection of Craters 更快、更准确地探测陨石坑

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-03 DOI: 10.1109/LGRS.2025.3557756

Sourish Chatterjee;Shayak Chakraborty;Pinaki Roy Chowdhury;Benidhar Deshmukh;Anirban Nath

{"title":"Toward Faster and Accurate Detection of Craters","authors":"Sourish Chatterjee;Shayak Chakraborty;Pinaki Roy Chowdhury;Benidhar Deshmukh;Anirban Nath","doi":"10.1109/LGRS.2025.3557756","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3557756","url":null,"abstract":"Impact craters are the most frequent geological features that a spacecraft may encounter while landing or navigating on planetary surfaces like those of Mars. A spacecraft’s Terrain-Relative Navigation (TRN) can be improved by automated extraction of these craters in real time. It is crucial to accurately pinpoint craters in the event of a soft landing, especially the smaller ones. This calls for an automated pipeline, which provides a fast and accurate detection of craters. The present research work makes use of the improved You Only Look Once (YOLO) version 8 for detecting craters from a Martian image dataset. The dataset is split into train, validation, and test sets, and the training set is also augmented for inducing variance into the model. The efficacy of the activation functions is tested by replacing the original SiLU from the YOLO backbone with ReLU, Mish, and SoftPlus activations. The unaltered backbone in itself manages to achieve very high accuracy as compared to the state-of-the-art (SOTA) techniques that have previously been used for crater detection. Mish activation shows the highest test <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>-score of 0.915, which is better than the originally used SiLU. The Mish-YOLO v8 manages to extract an average of 25 craters from single images in just 8.5 ms, which makes it the fastest existing crater detection pipeline having an <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>-score higher than 0.9, thereby making the proposed approach an excellent tool for automated crater detection from images.","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":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848795","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

SAU-GAN: A Shuffle Attention U-Net Generative Adversarial Network for GPR Inversion 一种用于探地雷达反演的洗牌注意力U-Net生成对抗网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-03 DOI: 10.1109/LGRS.2025.3557521

Meijia Huang;Jieyong Liang;Pingbao Yin;Xuming Zhu;Zhuo Jia

{"title":"SAU-GAN: A Shuffle Attention U-Net Generative Adversarial Network for GPR Inversion","authors":"Meijia Huang;Jieyong Liang;Pingbao Yin;Xuming Zhu;Zhuo Jia","doi":"10.1109/LGRS.2025.3557521","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3557521","url":null,"abstract":"Ground-penetrating radar (GPR) is widely used in geotechnical engineering investigations, construction quality assessment, and geological disaster surveys due to its high resolution, accuracy, and nondestructive testing capabilities. However, the accuracy of GPR inversion imaging is often compromised by climatic conditions (such as precipitation and temperature) and complex subsurface environments, leading to suboptimal performance. To address this issue, we propose a shuffle attention U-Net generative adversarial network (GAN) for GPR inversion imaging—SAU-GAN. This network consists of a generator and a discriminator. The generator features an encoder-decoder network enhanced with a shuffle attention mechanism, facilitating efficient feature extraction from B-scan images and aiding in the generation of permittivity models. The discriminator evaluates generated models against real ones, providing feedback to supervise the generator’s performance. Both the components use double normalization to stabilize parameters and convolutional outputs. In addition, a multiscale structural similarity (MS-SSIM) loss function enhances the existing loss function, significantly improving inversion results. Experiments with synthetic data demonstrate that SAU-GAN produces permittivity models with higher accuracy and clearer boundaries than existing methods. Even under interference, it is able to perform precise inversion, demonstrating outstanding robustness and generalization performance. We conduct a quantitative analysis of SAU-GAN using SSIM, PSNR, and MSE metrics, further validating its superior performance. When applied to real measured data, SAU-GAN also exhibits commendable performance, validating its effectiveness and practical value.","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":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848904","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

Unveiling the Correlation Between Lyapunov Coefficients and Deep Learning Performance Using Ceilometer Data 利用Ceilometer数据揭示Lyapunov系数与深度学习性能之间的相关性

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-02 DOI: 10.1109/LGRS.2025.3557150

Razvan Vasile Ababei;Silvia Garofalide;Georgiana Bulai;Gheorghe Dan Dimitriu;Silviu Gurlui;Marius Mihai Cazacu

{"title":"Unveiling the Correlation Between Lyapunov Coefficients and Deep Learning Performance Using Ceilometer Data","authors":"Razvan Vasile Ababei;Silvia Garofalide;Georgiana Bulai;Gheorghe Dan Dimitriu;Silviu Gurlui;Marius Mihai Cazacu","doi":"10.1109/LGRS.2025.3557150","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3557150","url":null,"abstract":"The planetary boundary layer (PBL) is a crucial parameter to investigate for characterizing the atmosphere, particularly concerning aerosol concentrations. Understanding the PBL allows us to estimate air quality, provide weather forecasts, and establish correlations with astronomical seeing conditions and atmospheric turbulence intensity. The PBL can be defined in many ways, but its importance remains constant as it is the atmospheric layer where most socioeconomic activities occur. In this letter, we present a method to predict the stochastic PBL height (SPBLH) using ceilometer data and a deep learning approach based on a fully connected neural network (NN). We found a correlation between the Lyapunov coefficient calculated for each SPBLH time series and the loss function, which is influenced by various factors such as atmospheric parameters, pollution, aerosols, and more. The performance of a typical NN used to predict a time series is significantly affected by the degree of chaos, quantified by the largest Lyapunov exponents (LLEs). Our results show a decrease in accuracy as a function of increasing LLE. Moreover, an increased number of virtual neurons in the NN can be detrimental to SPBLH prediction for the complex dynamics of the PBL due to atmospheric conditions and unforeseen events.","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":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845421","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

Fast Forward Modeling of 3-D Gravity Data for Curved Hexahedral Grid Based on Neural Network 基于神经网络的曲面六面体网格三维重力数据快速正演建模

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-02 DOI: 10.1109/LGRS.2025.3557187

Haoyuan He;Tonglin Li;Rongzhe Zhang;Guanwen Gu;Zhihe Xu;Teng Luo

{"title":"Fast Forward Modeling of 3-D Gravity Data for Curved Hexahedral Grid Based on Neural Network","authors":"Haoyuan He;Tonglin Li;Rongzhe Zhang;Guanwen Gu;Zhihe Xu;Teng Luo","doi":"10.1109/LGRS.2025.3557187","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3557187","url":null,"abstract":"Unstructured grids are widely used in the processing and interpretation of geophysical data with terrain due to their excellent ability to simulate shape. Among them, the efficiency of the curved hexahedral grid in its gravity forward modeling based on the isoparametric finite-element method is poor due to the complex transformations involving numerous morphological nodes, which limits its application to large-scale data. For this reason, combined with the deep learning technology, this letter proposes a fast forward method of 3-D gravity data for the curved hexahedral grid based on the backpropagation (BP) neural network. In the training phase, the method learns the complex mapping of curved hexahedral elements to their gravity sensitivities through the neural network, thereby achieving fast forward modeling during the prediction phase. Numerical examples show that the new method has good simulation accuracy and generalization ability. Under the premise that the training phase can be completed upfront with its cost excluded, its forward efficiency is tens of times higher than that of the isoparametric finite-element method. The successful application of the new method in the actual terrain model of Mount Taishan area in China further proves its practicality.","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":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830527","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

Long-Time Coherent Integration for High Maneuvering Weak Target Detection Based on SoKT-NuFFT 基于SoKT-NuFFT的高机动弱目标检测长时间相干集成

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-02 DOI: 10.1109/LGRS.2025.3557015

Lang Xia;Penghui Huang;Xiang-Gen Xia;Junli Chen;Peili Xi;Xiangcheng Wan;Jiaqi Wang

引用次数: 0

An Algorithm for Freeze/Thaw State Detection Using GNSS-R Reflectivity Time Series 基于GNSS-R反射率时间序列的冻融状态检测算法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-02 DOI: 10.1109/LGRS.2025.3557195

Jiaxing He;Nanshan Zheng;Rui Ding;Xuexi Liu;Jiawei Wang

{"title":"An Algorithm for Freeze/Thaw State Detection Using GNSS-R Reflectivity Time Series","authors":"Jiaxing He;Nanshan Zheng;Rui Ding;Xuexi Liu;Jiawei Wang","doi":"10.1109/LGRS.2025.3557195","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3557195","url":null,"abstract":"The detection of soil freeze-thaw (F/T) states is crucial for understanding surface hydrological processes, carbon cycle dynamics, and their climatic implications. Recent advancements in spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) have demonstrated significant potential for soil state classification. Surface reflectivity, derived from GNSS-R measurements, is a key parameter for distinguishing between frozen and thawed soil conditions. This study implements an edge detection algorithm to analyze reflectivity time series obtained from Cyclone Global Navigation Satellite System (CYGNSS) observations, enabling the estimation of soil F/T transition onset dates. The algorithm’s performance was validated by comparing the results with ERA5_Land surface temperature data, showing a mean absolute deviation (MAD) of 20.2 days in seasonal transition date estimates and achieving an overall detection accuracy of 88.95%. These validation results indicate strong consistency between the predicted and reference data, showing the algorithm’s efficacy in determining soil-state transitions. This work contributes to the advancement of soil F/T mapping techniques and enhances our understanding of seasonal soil-state dynamics.","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":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826491","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

DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection 红外小目标检测的动态注意力转换网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-02 DOI: 10.1109/LGRS.2025.3557021

Chen Hu;Yian Huang;Kexuan Li;Luping Zhang;Chang Long;Yiming Zhu;Tian Pu;Zhenming Peng

引用次数: 0

SAR-TinySNN: A Lightweight Spiking Neural Network for SAR Target Recognition SAR- tinysnn：一种用于SAR目标识别的轻量级脉冲神经网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-02 DOI: 10.1109/LGRS.2025.3557054

Junyu Wang;Hao Sun;Yuli Sun;Tao Tang;Lin Lei;Kefeng Ji

{"title":"SAR-TinySNN: A Lightweight Spiking Neural Network for SAR Target Recognition","authors":"Junyu Wang;Hao Sun;Yuli Sun;Tao Tang;Lin Lei;Kefeng Ji","doi":"10.1109/LGRS.2025.3557054","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3557054","url":null,"abstract":"Spiking neural networks (SNNs) are the third generation of neural networks that offer the advantages of low computational requirements, fast inference speed, and strong biological interpretability. This makes SNNs suitable for synthetic aperture radar (SAR) target recognition tasks, which are often constrained by limited computational power. This letter proposes SAR-TinySNN, a lightweight SNN architecture designed for SAR target recognition. Unlike existing SAR-related studies that predominantly rely on rate coding, SAR-TinySNN uses direct coding to encode SAR images, allowing for a more efficient coding method adapted to SAR images and achieving high target recognition accuracy, especially in scenarios with limited training samples. By integrating direct coding into a trainable SNN framework, SAR-TinySNN achieves competitive performance compared with traditional deep neural networks (DNNs) and deep SNNs on vehicle, aircraft, and ship SAR target recognition datasets, with faster inference times. The experimental results demonstrate the effectiveness of SAR-TinySNN for SAR target recognition.","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":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845423","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

MSHCCT: A Multiscale Compact Convolutional Network for High-Resolution Aerial Scene Classification 基于多尺度卷积神经网络的高分辨率航拍场景分类

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-01 DOI: 10.1109/LGRS.2025.3556373

Ali Jamali;Swalpa Kumar Roy;Bing Lu;Leila Hashemi Beni;Nafiseh Kakhani;Pedram Ghamisi

{"title":"MSHCCT: A Multiscale Compact Convolutional Network for High-Resolution Aerial Scene Classification","authors":"Ali Jamali;Swalpa Kumar Roy;Bing Lu;Leila Hashemi Beni;Nafiseh Kakhani;Pedram Ghamisi","doi":"10.1109/LGRS.2025.3556373","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3556373","url":null,"abstract":"The growing popularity of vision transformers (ViTs) in remote sensing image classification is due to their ability to effectively capture long-range dependencies. However, their high computational cost and memory footprint limit their applicability, particularly for small-scale datasets and resource-constrained environments. To address these challenges, we propose the multiscale multihead compact convolutional transformer (MSHCCT), a lightweight yet powerful model that integrates convolutional tokenization with small-scale ViTs to enhance multiscale feature representation while maintaining computational efficiency. Despite a modest increase in parameters and training time, MSHCCT achieves superior classification accuracy and robustness on high-resolution aerial scenes. Importantly, our approach eliminates the need for model pretraining, additional datasets, or multisensor data fusion, ensuring a computationally efficient and practical solution for remote sensing applications. The code will be made publicly available at <uri>https://github.com/aj1365/MSHCCT</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":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845536","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

Monitoring Forest Changes With Foundation Models and Sentinel-2 Time Series 利用Foundation模型和Sentinel-2时间序列监测森林变化

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-31 DOI: 10.1109/LGRS.2025.3556601

Jakub Sadel;Lukasz Tulczyjew;Agata M. Wijata;Mateusz Przeliorz;Jakub Nalepa

引用次数: 0