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6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Reddy, Allipurapu Meghanath Gangadhara, Sai Likhith Reddy, R. Ravinder Padmalatha, E. Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

remote sensing object detection plays a crucial role in environmental monitoring, yet traditional methods often face difficulties with varying object scales, intricate backgrounds, and densely populated small objects. This research study elaborates on various methodologies carried out in the remote sensing detection area, with the support of the latest advances in traditional and deep learning techniques. We make a critical presentation of CNNs, Swin Transformers, and hybrid models, their adaptations, benefits, and limitations. performance metrics along with adaptability across different datasets and environments are presented through comparative analysis of various models. The paper provides in-depth analysis into the ever-changing landscape of remote sensing technologies and the latest tangible applications regarding such technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design

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remote sensing 2025年第1期17卷 69-69页

作者： Shao, Yingzhao Wang, Junyi Han, Xiaodong Li, Yunsong Li, Yaolin Tao, Zhanpeng Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China China Acad Space Technol Xian Xian 710100 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

To meet the high-reliability requirements of real-time on-orbit tasks, this paper proposes a fault-tolerant reinforcement design method for spaceborne intelligent processing algorithms based on convolutional neural networks (CNNs). This method is built on a CNN accelerator using Field-Programmable Gate Array (FPGA) technology, analyzing the impact of Single-Event Upsets (SEUs) on neural network computation. The accelerator design integrates data validation, Triple Modular Redundancy (TMR), and other techniques, optimizing a partial fault-tolerant architecture based on SEU sensitivity. This fault-tolerant architecture analyzes the hardware accelerator, parameter storage, and actual computation, employing data validation to reinforce model parameters and spatial and temporal TMR to reinforce accelerator computations. Using the ResNet18 model, fault tolerance performance tests were conducted by simulating SEUs. Compared to the prototype network, this fault-tolerant design method increases tolerance to SEU error accumulation by five times while increasing resource consumption by less than 15%, making it more suitable for spaceborne on-orbit applications than traditional fault-tolerant design approaches.

关键词： spaceborne computing single-event upset convolutional neural network field-programmable gate array fault-tolerant design

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END-TO-END PROCESS ORCHESTRATION OF EARTH OBSERVATION DATA WORKFLOWS WITH APACHE AIRFLOW ON high performance computing

END-TO-END PROCESS ORCHESTRATION OF EARTH OBSERVATION DATA W...

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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Tian, Liang Sedona, Rocco Mozaffari, Amirpasha Kreshpa, Enxhi Paris, Claudia Riedel, Morris Schultz, Martin G. Cavallaro, Gabriele Univ Iceland Sch Engn & Nat Sci IS-107 Reykjavik Iceland Forschungszentrum Julich Julich Supercomp Ctr D-52428 Julich Germany Univ Twente NL-7514 AE Enschede Netherlands

ISBN: (纸本)9798350320107

Earth Observation (EO) data processing faces challenges due to large volumes, multiple sources, and diverse formats. To address this issue, this paper presents a scalable and parallelizable workflow using Apache Airflow, capable of integrating Machine Learning (ML) and Deep Learning (DL) models with Modular Supercomputing Architecture (MSA) systems. To test the workflow, we considered the production of large-scale Land-Cover (LC) maps as a case study. The workflow manager, Airflow, offers scalability, extensibility, and programmable task definition in Python. It allows us to execute different steps of the workflow in different high-performance computing (HPC) systems. The workflow is demonstrated on the Dynamical Exascale Entry Platform (DEEP) and J ulich Research on Exascale Cluster Architectures (JURECA) hosted at the J ulich Supercomputing Centre (JSC), a platform that incorporates heterogeneous JSC systems.

关键词： Workflows Deep Learning (DL) high-performance computing (HPC) remote sensing data

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A Novel Aerosol Retrieval Method Based on Joint Polarization and Intensity Data of Synchronization Monitoring Atmospheric Corrector (SMAC) Onboard high-Spatial-Resolution GFDM Satellite

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Ge, Bangyu Wang, Xia Li, Zhengqiang Zhou, Ping Li, Guoyuan Hou, Weizhen Qiu, Zhenwei Xiao, Chenchao Liu, Shuhan Yue, Qingxing Wang, Haofei Xie, Yisong Minist Nat Resources Peoples Republ China Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Aerosp Informat Res Inst Beijing 100101 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Key Lab Opt Calibrat & Characterizat Hefei 230031 Peoples R China China Meteorol Adm Natl Satellite Meteorol Ctr Nat Ctr Space Weather Innovat Ctr Fengyun Meteorol Satellite FYSICKey Beijing 100101 Peoples R China

Synchronization monitoring atmospheric corrector (SMAC) sensor is equipped with polarization and intensity data, providing the possibility of retrieving more accurate aerosol parameters for main sensor's atmospheric correction. In this study, a novel aerosol retrieval algorithm based on intensity and polarization data was proposed. First, we analyze and construct the ratio relationship of different intensity and polarization channels of SMAC on different surface types and analyze the correlation between these ratios and normalized difference vegetation index (NDVI) and scattering angle (SCA). Then, for the first time, high-precision surface prior knowledge for SMAC aerosol retrieval is constructed, and then, intensity and polarization data were used to retrieve high-precision aerosol products simultaneously. These results are compared and validated with Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based observations aerosol products, and aerosol optical depth (AOD) product comparison between the SMAC and MODIS showed that they had similar spatial distribution, scattered dots of them with a Pearson correlation coefficient (R) of 0.84 and a root-mean-square error (RMSE) of 0.11. Meanwhile, their differences were also analyzed. The validation between the ground-based sites and the SAMC retrieval results also showed a good performance with R of 0.88 and RMSE of 0.10. These results revealed that the new algorithm is an effective method for retrieving reliable AOD products for the main sensor's atmosphere correction.

关键词： Aerosols Satellite broadcasting MODIS Reflectivity Accuracy remote sensing Land surface Cloud computing Spatial resolution Monitoring Aerosol retrieval intensity polarization surface prior knowledge synchronization monitoring atmospheric corrector (SMAC)

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A Tiny-Siamese Network with Bilateral Channels for Aerial Optics remote sensing Images Change Detection 5

A Tiny-Siamese Network with Bilateral Channels for Aerial Op...

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5th International conference on Geology, Mapping and remote sensing (ICGMRS)

作者： Jing, Laishuo Sha, Jianjun Wang, Yanheng Gao, Jixin Xu, Hongbin Harbin Engn Univ Qingdao Innovat & Dev Base Qingdao Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

ISBN: (纸本)9798350365719;9798350365726

Deep learning plays an increasingly important role for change detection (CD) in aerial optics remote sensing images (AORSIs). Convolutional neural networks with superior performance usually require an increased number of convolutional layers. However, the expanded size of convolutional layers significantly increases memory consumption, posing a challenge for practical applications especially in scenarios with low computing power. A tiny-Siamese network with bilateral channels (TSNBC) is proposed for AORSIs-CD. We first design a bilateral feature extraction module with few parameters to extract both detailed and global features. Furthermore, a difference enhancement module is designed to reinforce the distinctions between detailed and global features of bi-temporal AORSIs. The experimental results on the public dataset show that the TSNBC proposed in this paper achieves the best performance across many measured indices.

关键词： aerial optics remote sensing difference enhancement siamese network bilateral feature extraction

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RSSRDiff: An Effective Diffusion Probability Model with Attention for Single remote sensing Image Super-Resolution 20th

RSSRDiff: An Effective Diffusion Probability Model with Atte...

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20th International conference on Intelligent computing (ICIC)

作者： Wei, Tian Zhang, Hanyi Xu, Jin Zhao, Jing Shen, Fei East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China State Grid Jiangsu Elect Power Co Ltd Nanjing Power Supply Branch Nanjing Peoples R China State Grid Jiangsu Elect Power Co Ltd Wuxi Power Supply Branch Wuxi Jiangsu Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9789819756087;9789819756094

Recently, optical remote-sensing images (RSIs) have found widespread applications in fields such as environmental monitoring and land cover classification. However, these images often suffer from limitations in imaging equipment and other factors, resulting in low-resolution images that are not ideal for image analysis. While existing image super-resolution (SR) algorithms can enhance image resolution, they are not specifically tailored to the characteristics of RSIs and fail to effectively recover high-resolution images. In this study, we propose a novel method called RSSRDiff, specifically designed to address the unique characteristics of RSIs using diffusion probability models. Our approach builds upon the SRDiff network, which has shown promise in the field of remote sensing. Additionally, we incorporate an attention mechanism to enhance the utilization of contextual information in low-resolution images. These improvements enable RSSRDiff to preserve more informative cues, leading to accurate SR results. To evaluate the performance, we conducted extensive experiments on four remote sensing datasets, including both simulated and real-world RSIs. The results demonstrate that RSSRDiff successfully restores high-quality SR images, showcasing its effectiveness in enhancing the resolution of RSIs.

关键词： Diffusion model Image super-resolution remote sensing Attenion mechanism

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A Novel Transformer-based Multiscale Siamese Framework for high-resolution remote sensing Change Detection

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IEEE Journal of Selected Topics in Applied Earth Observations and remote sensing 2025年 18卷 13087-13099页

作者： Wang, Liangjun Chen, Weitao Wang, Haoyi Chen, Zhengchao China University of Geosciences Faculty of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Airborne Remote Sensing Center Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Change detection (CD) in remote sensing based on deep convolutional neural networks (CNNs) and transformers has played a crucial role in surface monitoring and resource development. However, the inadequate extraction of local and global features, along with the insufficient utilization of multiscale features, constrains the ability to identify change regions at multiple scales. To resolve the aforementioned issue, we propose a transformer-based multiscale Siamese framework (TMSF) for high-resolution remote sensing CD, aiming to improve detection accuracy. First, we propose a dual-attention token-based (DAT-based) transformer module, which embeds attention tokens into the transformer, effectively integrating local and global features and thereby enhancing the discriminative power of the extracted features. Second, we design a multiscale feature adaptive fusion module (MFAFM) that incorporates a channel attention mechanism to adaptively integrate multiscale information, enabling more precise identification of change regions at various scales. We verify the effectiveness of the proposed method in improving CD accuracy on three widely used benchmark datasets, LEVIR-CD, WHU-CD and CDD. Experimental results show that our method outperforms ten state-of-the-art CD methods in terms of F1-score and intersection over union (IoU) metrics. Notably, compared to that of five multiscale-based methods, our proposed TMSF achieves superior performance while requiring only half the number of parameters and computational cost. Thus, the proposed model demonstrates a marked advancement in remote sensing CD. © 2008-2012 IEEE.

关键词： Deep neural networks

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Multiscale Feature Extraction-Based remote sensing Image Matching Network 4

Multiscale Feature Extraction-Based Remote Sensing Image Mat...

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4th International conference on Intelligent Communications and computing

作者： Tang, Haoyang Zhao, Ziqiao Gao, Yujie Bai, Zuqiang Yang, Dongfang Xian Univ Posts & Telecommun Sch Automat Xian Peoples R China Peoples Liberat Army Unit 96941 Xian Peoples R China Xian High Tech Res Inst Comp Image Proc Xian Peoples R China

ISBN: (纸本)9798331530372;9798331530365

This paper addresses the challenges of feature extraction and real-time matching in heterogeneous remote sensing images caused by the differing imaging principles of various sensors. We propose a remote sensing image matching network based on Multiscale feature extraction. The network establishes a matching framework for aerial and satellite images, employing a Multiscale Feature Enhancement Module (MSFEM) to extract scale-invariant features and perform salient feature point detection. This approach effectively captures local features and contextual information from heterogeneous images. Subsequently, the feature points and descriptors are decoded and fed into the SuperGlue feature matching network for alignment. Experimental results demonstrate that our proposed matching method improves matching accuracy by 13.79% compared to D2Net and by 2.86% compared to LoFTR, validating the network's potential for feature extraction and matching tasks in heterogeneous remote sensing imagery.

关键词： remote sensing Multiscale Features Feature Extraction Image Matching

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Bilinear Parallel Fourier Transformer for Multimodal remote sensing Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Chen, Yaxiong Wang, Qicong Zhao, Yichen Xiong, Shengwu Lu, Xiaoqiang Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Wuhan Coll Interdisciplinary Artificial Intelligence Res Inst Wuhan 430212 Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China

Vision Transformers (ViTs) have shown promise in multimodal fusion image classification, yet face performance challenges in complex remote sensing scenarios. Single fusion frameworks often fail to fully utilize multimodal diversity, and the uneven distribution of image categories complicates the accurate construction of spatial structures by Transformers. Additionally, traditional cross-entropy tends to favor majority classes, neglecting minority classes, resulting in suboptimal predictions and reduced overall accuracy (OA). To solve these challenges, we propose a novel deep neural network, a bilinear parallel Fourier Transformer (BPFT). We propose a novel dual-fusion feature interaction (DFFI) module that utilizes two distinct types of fused features for learning, namely the spatial-spectral fusion feature and the global fusion feature. Besides, we introduce a dual-feature interaction (DFI) module to improve the utilization of fused feature information. To enable the Transformer to better establish spatial structural relationships, we employ the Fourier transform in place of the self-attention mechanism. To address the focus on minority class labels, we propose an exponential label smoothing cross-entropy loss function. This loss function comprises two components: exponential cross-entropy and label smoothing. The exponential cross-entropy component applies a strong penalty to misclassified samples, thereby increasing attention on minority class labels. To validate the efficacy of our approach, extensive experiments are conducted across two multimodal remote sensing datasets: Augsburg and Berlin, encompassing hyperspectral imaging (HSI) data and synthetic aperture radar (SAR) data. The results of these experiments affirm the superior performance of our proposed BPFT model compared to existing state-of-the-art models in multimodal remote sensing image classification tasks.

关键词： Transformers remote sensing Feature extraction Image classification Data mining Artificial intelligence Fourier transforms Data models Convolutional neural networks Accuracy Cross-modal retrieval prior similarity saliency learning

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MA-YOLOv8 Algorithm for Mining Area Object Detection Based on high-Resolution remote sensing Images 20th

MA-YOLOv8 Algorithm for Mining Area Object Detection Based o...

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20th International conference on Intelligent computing (ICIC)

作者： Zhang, Yufang Su, Xiaojun Ma, Ming Inner Mongolia Univ Hohhot 010021 Peoples R China

ISBN: (纸本)9789819756742;9789819756759

remote sensing object recognition has been applied in the ecological environment of mining areas, exploring its potential applications in remote sensing target classification, detection, and monitoring of land surface cover and land use changes. However, existing target detection algorithms have not fully utilized the rich detailed information in remote sensing images. To exploit the feature information of remote sensing targets at different scales and achieve precise detection of ground objects, we propose the MA-YOLOv8 algorithm for ground object detection in open-pit mining areas based on high-resolution remote sensing images. Firstly, addressing the issue of insufficient multi-scale target detection capability of the YOLOv8 model on the MAOD dataset, this paper proposes two improvement structures: serialized ASPP structure and Dilated Spatial Pyramid Position Pooling structure (ASPP-SP). These two improvement structures can capture features of ground objects at different scales. Secondly, addressing the problem of missed and false detections of deformable targets in the MAOD dataset, this paper proposes the DC2f structure, which significantly improves the model's detection capability for deformable targets. By integrating these three improvements, the MA-YOLOv8 model is proposed, which effectively enhances the performance of multi-scale target detection, utilization of positional information, and detection of deformable targets. This study employs remote sensing methods such as unmanned aerial vehicles and satellites to collect data on open-pit mining areas and surrounding areas and constructs the MAOD dataset based on open-pit mining areas. Compared with YOLOv8, our proposed algorithm improves mAP@0.5 from 0.880 to 0.894 and mAP@0.5:0.95 from 0.658 to 0.692.

关键词： remote sensing object recognition deep learning mining area ecology atrous spatial pyramid pooling deep deformable convolution

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