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high performance and Disruptive computing in remote sensing: The Third Edition of the School Organized by the HDCRS Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

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IEEE GEOSCIENCE AND remote sensing MAGAZINE 2024年第1期12卷 199-204页

作者： Cavallaro, Gabriele Heras, Dora Blanco Maskey, Manil Forschungszentrum Julich D-52428 Julich Germany Univ Iceland IS-107 Reykjavik Iceland Univ Santiago De Compostela Ctr Singular Invest Tecnoloxias Intelixentes Santiago De Compostela 15782 Spain Natl Aeronaut Space Adm Huntsville AL 35812 USA

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State of the Art high-performance and high-Throughput computing for remote sensing Big Data

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IEEE GEOSCIENCE AND remote sensing MAGAZINE 2022年第4期10卷 125-149页

作者： Zhang, Sheng Xue, Yong Zhou, Xiran Zhang, Xiaopeng Liu, Wenhao Li, Kaiyuan Liu, Runze China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China Univ Derby Sch Comp & Engn Derby DE22 1GB England China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China

In recent years, with the increasing number of Earth observation satellites and the popularization and application of various sensors, remote sensing data have shown a rapid growth trend and present typical big data characteristics. The continuous enrichment of remote sensing data has provided large information resources for Earth science research and promoted the wide application of remote sensing technology in resources, ecology, environment, energy, health, urban management, and so on. However, mining information from multisource heterogeneous remote sensing big data, which requires a large amount of computing power, has many challenges in terms of generality, security, and timeliness. In this article, we summarize the existing research on high-performance computing (HPC) and high-throughput computing (HTC) technologies toward improving the processing efficiency of remote sensing big data. We also analyze the problems and challenges of HPC/HTC technologies in the storage, computation, and analysis of remote sensing big data. Finally, we predict the trend of remote sensing big data processing in the direction of HPC/HTC.

关键词： remote sensing Big Data Supercomputers Sensors Satellites Peer-to-peer computing Computer architecture

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high-performance and Disruptive computing in remote sensing HDCRS-A New Working Group of the GRSS Earth Science Informatics Technical Committee

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IEEE GEOSCIENCE AND remote sensing MAGAZINE 2022年第2期10卷 329-345页

作者： Cavallaro, Gabriele Heras, Dora B. Wu, Zebin Maskey, Manil Lopez, Sebastian Gawron, Piotr Coca, Mihai Datcu, Mihai Julich Supercomp Ctr AI & ML Remote Sensing Simulat & Data Lab Wilhelm Johnen Str D-52428 Julich Germany Univ Bonn Inst Geodesy & Geoinformat Scalable Machine Learning Remote Sensing Big Dat Bonn Germany Univ Santiago de Compostela Dept Elect & Comp Engn Santiago 15782 Spain Univ Santiago de Compostela Remote Sensing Comp Grp Santiago 15782 Spain Ctr Singular Invest Tecnoloxias Intelixentes CiTI Santiago De Compostela 15782 Spain Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Grenoble Alpes Grenoble Images Parole Signal Automat Lab Grenoble Inst Technol Grenoble France Univ Calif Los Angeles Dept Math Los Angeles CA 90024 USA Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Hyperspectral Comp Lab Badajoz Spain NASA Marshall Space Flight Ctr Huntsville AL 35808 USA Univ Las Palmas Gran Canaria Las Palmas Gran Canaria Spain Inst Appl Microelect Integrated Syst Design Div Las Palmas Gran Canaria 35001 Las Palmas Spain Polish Acad Sci Particle Astrophys Sci & Technol Ctr AstroCeNT In Nicolaus Copernicus Astron Ctr PL-00614 Warsaw Poland Polish Acad Sci Quantum Syst Informat Grp Inst Theoret & Appl Informat Gliwice Poland Mil Tech Acad Ferdinand I Comp Sci & Cyber Secur Lab Bucharest 050141 Romania Mil Tech Acad Ferdinand I Bucharest Romania German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany Univ Politech Bucharest Dept Appl Elect & Informat Engn Fac Elect Telecommun & Informat Technol Bucharest 061071 Romania Swiss Fed Inst Technol Swiss Fed Inst Technol Zurich Switzerland Telecom Paris Paris Inst Technol ParisTech Paris France

The high-performance and Disruptive computing in remote sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and remote sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.

关键词： remote sensing

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Efficient distributed matrix for resolving computational intensity in remote sensing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 166卷

作者： Zou, Weitao Li, Wei Wang, Zeyu Pei, Jiaming Lou, Tongtong Chen, Guangsheng Jing, Weipeng Zomaya, Albert Y. Northeast Forestry Univ Coll Comp & Control Engn 26 Hexing Rd Harbin 150040 Peoples R China Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Sydney NSW 2006 Australia

remote sensing analysis is a dominant yet time-consuming part of geospatial applications. The performance can be optimized based on distributed computing, but current systems still face significant challenges. Firstly, the spatial characteristics of remote sensing data lead to an uneven distribution of computational intensity (CIT), which characterizes computing loads, including computation and IO, indifferent spatial domains. Secondly, it is hard to achieve load-balancing without introducing new computational costs, thus increasing the CIT and reducing the overall performance. Therefore, resolving CIT by decreasing and balancing it is an important research issue for distributed remote sensing computing. This paper proposes LBM-RS, an efficient distributed framework based on load-balancing matrix computing for remote sensing. It implements remote sensing applications based on the distributed matrix, representing the algorithms with a matrix computation and constructing multi-dimensional spatial domains to model computational costs for matrix operation tasks. It resolves the CIT with the minimum computation load and dynamic spatial domain decomposition strategy to support global load balancing. We also exploit the IO efficiency from the task staging strategy and the cache-aware memory structure for remote sensing data. In this way, it can reduce the bandwidth burden and memory access frequency, thus decreasing the overall CIT. Finally, we evaluate the proposed approach on both real and synthetic datasets, and the results demonstrate significant advantages in computation and communication efficiency compared to the benchmarks.

关键词： remote sensing data Computational intensity Load balancing Distributed framework Distributed matrix

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high-performance On-Orbit Intelligent computing and Real-Time Services for remote sensing Satellites Based on Large-Scale computing Power in Space

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IEEE Access 2025年 13卷 92114-92133页

作者： Guoping Wang Gang Wan Zhijuan Su Yang Wang Yutong Jia Gong Li Shi Liang Department of Surveying and Mapping and Space Environment Space Engineering University Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Target Cognition and Application Technology Beijing China Beijing Institute of Space Mechanics and Electricity Beijing China School of Artificial Intelligence Xidian University Xi’an China

The rapid advancement in Earth observation systems and satellite networks driven by commercial space has led to a significant increase in the number of remote sensing satellites, thereby establishing the groundwork for all-weather, continuous, cluster-collaborative observation. Following anti-irradiation hardening measures, low-cost commercial off-the-shelf (COTS) devices are effectively operated within intricate space environments, leading to substantial enhancements of the on-orbit computing capabilities of individual satellites. The onboard-distributed processing framework can be leveraged to fully utilize wide-area scattered space computing resources; accordingly, it is possible to automatically establish a network of neighboring satellites through inter-satellite communication links to construct a spatially distributed and computationally cooperative cloud computing environment in space. This enables the sharing of space computing resources and facilitates satellite-based mission coordination, reducing reliance on ground-based remote sensing satellite services while enhancing the response efficiency of remote sensing application services. The present paper provides a comprehensive review of the current development status, existing challenges, and problems faced by space-based, on-orbit remote sensing processing platforms and onboard intelligent processing algorithms; we start with the immediate application requirements of the remote sensing satellites used in in-orbit processing. In view of the anticipated surge in large-scale computing resources in space, a distributed processing architecture for remote sensing satellite data, based on large-scale onboard computing power, is constructed. The architecture is designed in detail, with attention paid to the physical architecture, the software architecture, the workflow, and the key technologies. Finally, three real-time service scenarios that align with the construction content of an ultra-low-orbit satellite cons

关键词： Satellites remote sensing Space vehicles Real-time systems Satellite broadcasting Satellite constellations Earth Software Hyperspectral imaging Artificial intelligence

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Brain-Inspired Online Adaptation for remote sensing With Spiking Neural Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Duan, Dexin Liu, Peilin Hui, Bingwei Wen, Fei Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Brain Inspired Applicat Technol Ctr BATC Shanghai 200240 Peoples R China Natl Univ Def Technol ATR Key Lab Sch Elect Sci & Engn Changsha 410073 Peoples R China

On-device computing, or edge computing, is becoming increasingly important for remote sensing, particularly in applications like deep network-based perception on on-orbit satellites and unmanned aerial vehicles (UAVs). In these scenarios, two brain-like capabilities are crucial for remote sensing models: 1) high energy efficiency, allowing the model to operate on edge devices with limited computing resources, and 2) online adaptation, enabling the model to quickly adapt to environmental variations, weather changes, and sensor drift. This work addresses these needs by proposing an online adaptation framework based on spiking neural networks (SNNs) for remote sensing. Starting with a pretrained SNN model, we design an efficient, unsupervised online adaptation algorithm, which adopts an approximation of the backpropagation through time (BPTT) algorithm and only involves forward-in-time computation that significantly reduces the computational complexity of SNN adaptation learning. Besides, we propose an adaptive activation scaling (AAS) scheme to boost online SNN adaptation performance, particularly in low time-steps. Furthermore, for the more challenging remote sensing detection task, we propose a confidence-based instance weighting scheme, which substantially improves adaptation performance in the detection task. To our knowledge, this work is the first to address the online adaptation of SNNs. Extensive experiments on seven benchmark datasets across classification, segmentation, and detection tasks demonstrate that our proposed method significantly outperforms existing domain adaptation and domain generalization approaches under varying weather conditions. The proposed method enables energy-efficient and fast online adaptation on edge devices, and has much potential in applications such as remote perception on on-orbit satellites and UAVs. The code is available at https://***/ThunderDavid/OASNN.

关键词： Adaptation models Neurons Computational modeling remote sensing Brain modeling Satellites Meteorology Energy efficiency Approximation algorithms Sensors Brain-inspired computing neuromorphic computing remote sensing image processing spiking neural network (SNN) unsupervised domain adaptation (UDA)

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A disparity-aware Siamese network for building change detection in bi-temporal remote sensing images

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PHOTOGRAMMETRIC RECORD 2024年第187期39卷 528-548页

作者： Li, Yansheng Li, Xinwei Chen, Wei Zhang, Yongjun Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

Building change detection has various applications, such as urban management and disaster assessment. Along with the exponential growth of remote sensing data and computing power, an increasing number of deep-learning-based remote sensing building change detection methods have been proposed in recent years. Objectively, the overwhelming majority of existing methods can perfectly deal with the change detection of low-rise buildings. By contrast, high-rise buildings often present a large disparity in multitemporal high-resolution remote sensing images, which degrades the performance of existing methods dramatically. To alleviate this problem, we propose a disparity-aware Siamese network for detecting building changes in bi-temporal high-resolution remote sensing images. The proposed network utilises a cycle-alignment module to address the disparity problem at both the image and feature levels. A multi-task learning framework with joint semantic segmentation and change detection loss is used to train the entire deep network, including the cycle-alignment module in an end-to-end manner. Extensive experiments on three publicly open building change detection datasets demonstrate that our method achieves significant improvements on datasets with severe building disparity and state-of-the-art performance on datasets with minimal building disparity simultaneously.

关键词： bi-temporal high-resolution remote sensing images building change detection disparity-aware Siamese network

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LuoJiaAI:A cloud-based artificial intelligence platform for remote sensing image interpretation

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Geo-Spatial Information Science 2023年第2期26卷 218-241页

作者： Zhan Zhang Mi Zhang Jianya Gong Xiangyun Hu Hanjiang Xiong Huan Zhou Zhipeng Cao State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Department of Land-Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong KongChina

The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new *** existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation,which ignores remote sensing data characteristics such as large image size,large-scale change,multiple data channels,and geographic knowledge embedding,thus impairing computational efficiency and *** construct a LuoJiaAI platform composed of a standard large-scale sample database(LuoJiaSET)and a dedicated deep learning framework(LuoJiaNET)to achieve state-of-the-art performance on five typical remote sensing interpretation tasks,including scene classification,object detection,land-use classification,change detection,and multi-view 3D *** details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial *** addition,LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium(OGC)standards for better developing and sharing Earth Artificial Intelligence(AI)algorithms and products on benchmark *** narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism,showing great potential in high-precision remote sensing mapping applications.

关键词： Artificial intelligence cloud computing platform remote-sensing intelligent interpretation sample database deep learning framework

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CNN-transformer dual branch collaborative model for semantic segmentation of high-resolution remote sensing images

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PHOTOGRAMMETRIC RECORD 2025年第189期40卷

作者： Zhu, Xiaotong Peng, Taile Guo, Jia Wang, Hao Cao, Taotao Sch Huaibei Normal Univ Coll Comp Sci & Technol Huaibei Peoples R China

high-resolution remote sensing images play an important role in geological surveys, disaster detection, and other fields. However, highly imbalanced ground target classes and easily confused small ground targets pose significant challenges to the semantic segmentation task. We propose IC-TransUNet, a new dual branch model based on an encoder-decoder structure that fully exploits the advantages of convolutional neural networks and transformers and considers both detailed information and semantic information capture. Specifically, a lightweight CSwin transformer and InceptionNeXt are used as the dual branch backbone of the model. To further improve the model performance, first, we designed the InceptionNeXt-CSwin Transformer Fusion Module (ICFM) and Edge Enhancement Module (EEM) to guide the dual branch backbone to obtain features. Second, a detachable Spatial-channel Attention Fusion Module (SCAFM) is designed to be flexibly inserted into multiple positions of the model. Finally, we designed a decoder with significant performance based on a global local transformer block, SCAFM, and a multilayer perceptron segmentation head. IC-TransUNet achieved highly competitive performance in experiments on the Vaihingen and Potsdam datasets from the International Society for Photogrammetry and remote sensing.

关键词： CNN dual encoder high-resolution remote sensing images semantic segmentation transformer transformer

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RNAF-YOLO: a model for highlighting difficult-to-detect targets in remote sensing images

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第3期36卷 035405-035405页

作者： Wang, Manyi Gao, Weiwei Fang, Yu Liu, Xintian Jin, Xiaoyi Shanghai Univ Engn Sci Inst Mech & Automot Engn Shanghai 201620 Peoples R China

Challenges like scale variations, shape diversity, complex backgrounds, and sample imbalance in remote sensing images make some targets difficult to detect. Consequently, a model called RNAF-You Only Look Once (YOLO) is proposed, combining a composite convolution module with an Intersection over Union (IoU)-based weighted loss function for remote sensing object detection. A composite convolution module, RepFocalNet, is designed and incorporated into the backbone network to replace the original C2f layer, enhancing multi-scale modeling and feature extraction capabilities in complex backgrounds. Adaptive spatial correlation pyramid attention is introduced after the ninth layer, enhancing sensitivity to subtle features and improving small object detection. Furthermore, Focal Inner Soft IoU is designed to replace the original loss function. Ablation experiments were conducted on the DIOR dataset to verify the effectiveness of each module. Following this, the proposed method was compared with several leading methods to further evaluate its performance. Compared to the YOLOv8 model, RNAF-YOLO improved mAP@50 by 1.5% and increased recall by 3%. Additionally, the classification accuracy for the bridge and vehicle categories increased by 4.2% and 8.9%. Compared to other methods, RNAF-YOLO demonstrates superior performance across multiple classification accuracy metrics. As a consequence, the proposed method demonstrates superior performance in remote sensing object detection, effectively highlighting difficult-to-detect targets.

关键词： YOLOv8 ASCP attention remote sensing object detection DIOR dataset

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