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检索条件"任意字段=Conference on High-Performance Computing in Remote Sensing IV"

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Ship Type Recognition Method Based on Multi-view Learning 3

Ship Type Recognition Method Based on Multi-view Learning

引用

3rd International conference on Artificial Intelligence, Automation, and high-performance computing, AIAHPC 2023

作者： Li, Tianjiao Liu, Zhonglin Wang, Licai Yu, Jintao North China Institute of Computing Technology Smart Plat Rd Department Beijing100083 China China Judicial Big Data Research Institute Co. Ltd. Beijing100043 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Aiming at the problem of low reliability of ship model recognition from remote sensing images, we propose a ship model recognition method based on multi-view learning. Firstly, the multi-view feature data is constructed by different feature operators. Then the single feature view data is used to train SVM classifier respectively while the multi-view data is fused and used to train classifier by CPM-Nets. Finally, we fuse the results of single feature view classifier and multi-feature view classifier by classifier aggregation to enhance the accuracy rate. Experiments show that the proposed method can improve the accuracy of ship type recognition. © 2023 SPIE.

关键词： remote sensing

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Research on the Application of Drone remote sensing and Target Detection in Civilian Fields 5

Research on the Application of Drone Remote Sensing and Targ...

引用

5th International conference on high performance Big Data and Intelligent Systems, HDIS 2023

作者： Gao, Xiaochen Bai, Xiangyu Zhou, Kexin Key of Wireless Networking and Mobile Computing College of Computer Inner Mongolia University Inner Mongolia Hohhot China

ISBN: (纸本)9798350394160

Unmanned aerial vehicle (UAV) remote sensing has evolved as an efficient means of geospatial data acquisition. This paper provides a comprehensive review of the key technologies enabling intelligent analysis of UAV remote sensing images, specifically payload technology and object detection algorithms. Main payload systems including optical, multispectral, hyperspectral, SAR, and LiDAR are introduced, with analyses of their working principles, performance benchmarks, and suitable application scenarios. In terms of object detection, traditional methods and emerging deep learning-based techniques are discussed and compared. The superior capabilities of anchor-free and Transformer-based detectors are analyzed through representative algorithms like CenterNet, FCOS, and DETR. Additionally, case studies demonstrate the broad applications and profound value of UAV remote sensing with intelligent target detection in areas such as precision agriculture, environmental conservation, infrastructure monitoring, and emergency response. Overall, this paper offers an in-depth examination of the landscape and trends of core technologies augmenting UAV remote sensing, providing guidance for future research and applications. ©2023 IEEE.

关键词： Object detection

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A Multi-Level Parallel Algorithm for Detection of Single Scatterers in SAR Tomography 33

A Multi-Level Parallel Algorithm for Detection of Single Sca...

引用

33rd Euromicro International conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Russo, Massimiliano Nisar, Mehwish Pauciullo, Antonio Imperatore, Pasquale Lapegna, Marco Romano, Diego University of Naples Federico II Dept. of Mathematics and Applications Naples Italy National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (纸本)9798331524937

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth's surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous high-performance computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring. © 2025 IEEE.

关键词： Light scattering

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Vision Transformers for remote sensing (ViToRS) Image Scene Classification 1

引用

4th International conference on Computer Vision, high-performance computing, Smart Devices, and Networks, CHSN 2023

作者： Gharai, Pankaj Kumar Shashi, Mogalla Department of CS and SE Andhra University College of Engineering Visakhapatnam India

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

Building deep learning models to perform computer vision tasks in remote sensing with higher accuracy using lesser computational resources and time has always been a challenge. Instead of using a single large-scale dataset of high spatial resolution for complete model training, this research divides the training into two parts. First part of training uses a single dataset of ‘low spatial resolution and high volume’, while second training part retrains the model on ‘high spatial resolution and low volume’ label-specific datasets. This paper proposes a method that comparatively evaluates 13 different vision transformer architectures and selects the best ones for building a composite model for remote sensing image scene classification. An efficient way to use transfer learning through training pretrained models and retraining fine-tuned models using the proposed method is discussed. Multiple label-specific vision transformer architecture models are run in parallel as a composite architecture to achieve higher accuracy for all class/labels, rather than running a single model for all class/labels with lower accuracy for some of the class/labels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Design of a high-performance Parallel computing Platform for Spaceborne Intelligent Programmable PSOC 7

Design of a High-Performance Parallel Computing Platform for...

引用

7th International conference on Advanced Electronic Materials, Computers, and Software Engineering, AEMCSE 2024

作者： Duan, Weiming Zhang, Peng Zhang, Xinglong Tan, Chao Zhu, Qixing Electronic Information Engineering Department Shandong Aerospace Electronic Technology Research Institute Shandong Yantai264006 China

ISBN: (纸本)9781510681866

The "satellite data acquisition and ground processing" mode is difficult to meet the characteristics of detection and recognition in high real-time tasks such as target tracking and disaster detection caused by the large width, high resolution, and massive data of remote sensing images themselves. Propose a master-slave parallel computing architecture based on switching nodes. This architecture uses domestically produced PSOC (Programmable System-On-Chip) intelligent heterogeneous AI(Artificial Intelligence) chips, combined with SRIO(Serial Rapid IO) and Ethernet buses to achieve decoupling between broadcast image data distribution and parallel scheduling of MPI(Message Passing Interface) systems. On the basis of better utilizing the computing performance of single chip processors, it explores parallel computing modes with higher computing power and reliability. This mode enables image detection algorithms with large-scale and complex operator characteristics to be deployed and applied faster and more conveniently in spaceborne environments, which has certain reference significance and research value. © 2024 SPIE.

关键词： Message passing

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A Novel Framework for Classifying remote sensing Images using Convolutional Neural Networks

A Novel Framework for Classifying Remote Sensing Images usin...

引用

2024 International conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Manoharan, Thiyagarajan Basha, Shaik Hedayath Murugan, J.Senthil Suja, G.P. Rajkumar, R. Srimathi, S. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India R.M.K. College of Engineering and Technology Department of Ece India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Cse Avadi Chennai India Muslim Arts College Thiruvithancode Department of Computer Science Kanyakumari629174 India Rajarajeswari College of Engineering Computer Science and Engineering Mysore Road Bengaluru74 India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350359688

The classification of Hyperspectral remote sensing Images (HRSIs) using Convolutional Neural Networks (CNNs) has gained significant. Due to excessive dimensional data and a lack of sample training examples, the Hughes phenomenon is frequently seen in hyperspectral remote sensing photos. In the realm of high-dimensional information processing, it is worth noting that there is a notable consumption of time and computing power. Additionally, there is a possibility that the features extracted may not accurately represent the data, leading to suboptimal classification efficiency and accuracy. In this research, a novel framework called GCNN is proposed, which combines the concepts of global context spatial attention (GCSA) and three-dimensional convolutional neural network (3DCNN). The GCNN framework aims to enhance the performance of spatial attention and convolutional neural networks by leveraging global context information. By integrating GCSA and 3DCNN, GCNN offers a promising approach for various applications in computer vision and image analysis. The mix-up operation is a commonly employed technique in the field of remote sensing image analysis. Its primary purpose is the spatially heterogeneous data available in these photos, and improve it. This is achieved by transforming the discrete sample space into a continuous one, thereby promoting improved flatness in the local vicinity of the data space. The integration of GCSA into the network allows for the encoding of contextual information from remote sensing scene images into the local features. The findings of this study indicate that the utilization of CNN for remote sensing image recognition yields significantly higher accuracy compared to traditional image recognition models. The recognition accuracy achieved by CNN in this context is reported to be as high as 97%. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on crop remote sensing image segmentation method integrating CNN and transformer

Research on crop remote sensing image segmentation method in...

引用

2024 International conference on Cloud computing, performance computing, and Deep Learning, CCPCDL 2024

作者： Guo, Wenjing Jin, Yuan Cheng, Xiaodong College of Electronic Information Engineering Inner Mongolia University Hohhot010000 China

ISBN: (纸本)9781510683044

Currently, when performing pixel-level semantic segmentation of crop planting in high-resolution images, it is difficult for deep convolutional neural networks to simultaneously capture global features and local detailed features at multiple scales in space. This can lead to blurred boundary contours between different farmland plots, as well as lower integrity within similar farmland areas. In view of the above research content, a model that integrates Transformer and CNN is proposed to classify and identify crops in the study area, using drone remote sensing images from a competition as the data source. (1) The model adopts a multi-level skip-connected encoder-decoder network architecture. The encoding part of the model uses an improved MobileNetV2 as the front-end feature extractor to extract local detail features, and then inputs the extracted features into Vision Transformer for global feature capture and further processing, thereby capturing details while retaining global context information. (2) The decoding part adopts the design of UNet, extracting features from different levels of the encoding part and directly transferring them to the corresponding levels of the decoding part through skip connections to ensure the retention of detail information, and using upsampling layers to gradually restore the spatial resolution of the image. In an experiment on a public competition dataset, the experimental results show that the MIoU of the network proposed in this paper reaches 85.96%, the PA reaches 92.30%, and the Dice value reaches 0.922, which has the highest segmentation accuracy compared with the comparison network. © 2024 SPIE.

关键词： Semantic Segmentation

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Generative Adversarial Network Based NDVI Super-resolution Reconstruction 12

Generative Adversarial Network Based NDVI Super-resolution R...

引用

12th IEEE Global conference on Consumer Electronics, GCCE 2023

作者： Sun, Zhengbao Pan, Hongyu Yue, Kun Yunnan University School of Engineering Kunming China Chinese Academy of Sciences Aerospace Information Reasearch Institute Beijing China Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350340181

Low resolution satellite remote sensing images remains a challenge for obtaining high-precision Normalized Difference Vegetation Index (NDVI), and contain some important vegetation cover information for supporting environmental analyses of the surface in large-scale regions, since high-resolution satellite remote sensing observations are still incomplete nowadays. In this paper, we consider the spatial resolution and time series characteristics of remote sensing images, and propose a super-resolution reconstruction algorithm for NDVI fused with Time features by optimizing the Super-Resolution Generative Adversarial Network (TSRGAN). Empirical research in the Salween River estuary area shows that the proposed approach has performance significant advantages in terms of PSNR and SSIM. © 2023 IEEE.

关键词： remote sensing

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YOLO-RS: An Efficient YOLO-Based Approach for remote sensing Object Detection 12

YOLO-RS: An Efficient YOLO-Based Approach for Remote Sensing...

引用

12th International conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Hashmi, Hina Dwivedi, Rakesh Kumar, Anil College of Computing Sciences & Information Technology Teerthanker Mahaveer University Moradabad India Dehradun India

ISBN: (纸本)9798350369861

This research introduces 'YOLO-RS,' an efficient object detection system based on YOLO tailored for remote sensing applications. YOLO-RS excels in real-time object detection in satellite and aerial imagery, optimizing performance while maintaining high accuracy. This study evaluates YOLO-RS on diverse remote sensing datasets, showcasing its precision and recall. The framework's speed and adaptability make it a valuable tool for tasks such as land cover mapping and disaster response in remote sensing scenarios. In this analysis, we have chosen the SENTINEL-2 dataset. With the help of the SENTINEL-2 dataset, we have evaluated the performance and accuracy of YOLOv3 and analyzed its strengths and weaknesses. Using the results obtained from the implementations, we determined and optimized the detection of building and trees in satellite imagery. The computed results indicate an average accuracy of 85.6%, precision of 98.9%, and recall of 86.6%. © 2023 IEEE.

关键词： Object detection

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Shadow Detection of remote sensing Image by Fusion of Involution and Shunted Transformer 6th

Shadow Detection of Remote Sensing Image by Fusion of Involu...

引用

6th Chinese conference on Pattern Recognition and Computer Vision (PRCV)

作者： Wang, Yifan Wang, Jianlin Huang, Xian Zhou, Tong Zhou, Wenjun Peng, Bo Southwest Petr Univ Sch Comp Sci Chengdu Peoples R China Sichuan Xinchuan Aviat Instrument Co Ltd Guanghan Peoples R China

ISBN: (纸本)9789819984619;9789819984626

In the field of remote sensing image analysis, this paper addresses the challenges of accurately detecting edge details and small shadow regions through the introduction of STAISD-Net. This end-to-end network synergistically combines the capabilities of CNN and Transformer to enhance shadow detection accuracy. Our network architecture incorporates several innovations. In the encoder, we propose an improved multi-scale asymmetric involution to extract detailed features across multiple scales. Additionally, we improve Shunted Transformer to extract global features, generating four-scale global feature information. In the decoder, we employ bilinear interpolation for upsampling and skip connections for fusing the high-level and low-level image features. Finally, we generate shadow masks by integrating feature maps of different scales. Comprehensive experiments conducted on the Aerial Imagery dataset for Shadow Detection (AISD) validate the effectiveness of STAISD-Net, showing that when compared to several state-of-the-art methods, our method demonstrates superior performance, achieving higher accuracy and the shadow detection results are more consistent with actual shadows.

关键词： Shadow detection remote sensing image Involution Shunted Transformer Multiscale feature

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