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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing"

共 4031 条记录，以下是141-150 订阅

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Parallel Optimization of remote sensing image Geometric Correction on DCU 5

Parallel Optimization of Remote Sensing Image Geometric Corr...

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5th International conference on Computer Engineering and Application (ICCEA)

作者： Liu, Runjie Wang, Siqi Zhengzhou Univ Natl Supercomp Ctr Zhengzhou Zhengzhou Henan Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Henan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Geometric correction is essential in the preprocessing of remote sensing images, ensuring the precision and reliability of spatial data, crucial for further analysis such as target recognition and feature extraction. Nevertheless, handling a lot of high-resolution images often encounters computational inefficiencies and limited real-time processing capabilities. This paper introduces a parallel optimization approach for geometric correction on multi-DCU heterogeneous clusters, leveraging a detailed thread mapping strategy tailored to the DCU architecture. It capitalizes on the specific hardware characteristics and execution models of the DCU for parallel processing. The study delves into the common bicubic interpolation resampling method used in geometric correction, optimizing thread mapping to match the DCU's computational and storage architecture. This method allows a single thread to handle multiple pixel computations, enhancing data reuse during interpolation. The research accomplishes parallel geometric correction processing on DCU and significantly curtails memory access through optimization, improving algorithmic efficiency. Comparative experimental outcomes show that the proposed parallel strategy markedly outperforms traditional serial CPU processing, multicore CPU processing, and unoptimized DCU acceleration, evidencing substantial speedup across processing different image sizes.

关键词： Geometric correction Parallel computing DCU HIP

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Research on Object-Oriented Classification Technology for remote sensing imagery of Coastal Zone 11th

Research on Object-Oriented Classification Technology for Re...

引用

11th International conference on Signal and Information processing, Network and Computers, ICSINC 2023

作者： Yize, Dong Rui, Zhang Haitao, Wang Chao, Wang Xianglei, Kong Lele, Yao Institute of Spacecraft Application System Engineering CAST No. 104 Youyi Road Haidian Beijing China

ISBN: (纸本)9789819721191

Terrain identification of coastal is of great significance for coastal development activities and coastal terrain survey in overseas areas. However, due to the complex characteristics of coastal features, the use of remote sensing images for automatic feature classification and recognition has become a current research hotspot. Utilizing the homogeneous and homogeneous spectral characteristics of hyperspectral remote sensing images and the characteristics of coastal zone elements, an object-oriented shoreland classification technique is proposed after performing operations such as atmospheric correction and image enhancement preprocessing on hyperspectral remote sensing data, which enables automatic identification and extraction of feature types and geomorphological information in the coastal zone. This method overcomes the limitation of the traditional hyperspectral remote sensing classification method, which takes image element as the processing unit, and synthesizes the spatial characteristics and spectral characteristics of the features, which greatly reduces the classification spots and significantly improves the classification effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coastal zones

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SAR Ship Classification based on Second-order Synchrosqueezing Transform and HOG Feature

SAR Ship Classification based on Second-order Synchrosqueezi...

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9th International conference on Signal and image processing (ICSIP)

作者： Li, Dongrui Zhao, Zhichun Shenzhen MSU BIT Univ Fac Engn Shenzhen Peoples R China

ISBN: (纸本)9798350350920

Accurate classification and identification of vessels in remote sensing satellite imagery is critical for ocean monitoring and resource management. The ability to extract information from remote-sensing data is of paramount importance. To exploit the non-stationary characteristics of synthetic aperture radar (SAR) target, a comprehensive SAR ship recognition framework is designed by combing the second-order synchrosqueezing transform (SST), an effective non-stationary signal processing tool, with the histogram of oriented gradient (HOG) feature in this paper. Firstly, the second-order SST is performed on SAR images to describe the non-stationary characteristics of ships at different times and frequencies. Secondly, HOG features are utilized to effectively extract the non-stationary information of SAR ships and provide more discriminative input for the deep learning network. Then, the optimal ResNet model is selected as the convolutional neural network (CNN) classifier to automatically fuse the non-stationary features and abstract features of SAR ships. Experiments on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) show that the proposed method achieves accurate classification and outperforms the state-of-the-art (SOTA) CNN-based methods in terms of robustness and generalization ability. The positive effect of non-stationary characteristics on SAR ship classification is verified.

关键词： Synthetic aperture radar (SAR) ship classification non-stationary signal processing time-frequency analysis (TFA) histogram of oriented gradient (HOG) convolutional neural network (CNN)

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A Robust Algorithm for Reading recognition of Pointer Instrument with Local Matching 12

A Robust Algorithm for Reading Recognition of Pointer Instru...

引用

SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Niu, Tun Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

The inspection and recognition of readings on pointer instruments are important tasks in power inspection. Due to the diversity and the complex dial information of pointer instruments, the adaptability and low accuracy of existing methods are not enough. In this paper, we proposed a pointer instrument reading recognition algorithm based on local fast matching. Firstly, YOLOv8 is used to locate and segment the instrument in the inspection image. The positions and values of key scale are marked in the template image. Based on the key local information, a mapping relationship is established between the instrument location region in the inspection image and the template image. Then, the Hough transform is used to detect the position of the pointer, map it to the template image, and finally calculate the instrument reading based on the relative positions of the key graduations on the template image and the mapped pointer position. Experimental results demonstrate that the proposed method achieves a relative error within 5% for pointer instrument reading recognition in indoor substations. © 2024 SPIE.

关键词： Inspection

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A method for cloud removal in remote sensing images based on attention mechanism and residual symmetric connection structure 12

A method for cloud removal in remote sensing images based on...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Guan, Kang Cheng, Qing Ye, Fan Wang, Haorui China University of Geosciences Wuhan School of Computer Science Wuhan China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Optical remote sensing images have become an important data source for studying the characteristics of the Earth's surface by their wide coverage, high spatial and temporal resolution, and information richness. However, in practical applications, optical remote sensing images are often affected by cloud occlusion, resulting in loss of surface information and errors. To this end, we propose a generative adversarial network (AM-RSC-GAN) based on the attention mechanism and residual symmetric connectivity structure. AM-RSC-GAN uses a multi-scale feature fusion module and the channel spatial attention mechanism to effectively improve the network's extraction and fusion of global and local information of the feature image and adopts the residual symmetric connectivity structure to enabling the network to achieve a good performance between effectively removing the cloud layer and retaining the original detail information. The residual symmetric connection structure enables the network to achieve a good balance between effective cloud removal and preservation of original detail information, better preserving the detail information of the original image and achieving faster convergence speed. We experimentally compare with several previous cloud removal models on the open-source RICE dataset and the WHUS2-CR dataset. The results show that AM-RSC-GAN achieves better results in both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), proving that the model has excellent cloud removal performance. © 2024 SPIE.

关键词： Optical remote sensing

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UHV transmission line monitoring based on satellite remote sensing

UHV transmission line monitoring based on satellite remote s...

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2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Zhang, Yunfeng Zhang, Peng Shen, Jianliang Wei, Wenli Yue, Lingping Zhang, Jun State Grid Huzhou Power Supply Company Zhejiang Huzhou313000 China Center of Mass Entrepreneurship Innovation State Grid Zhejiang Electric Power Co. Ltd Zhejiang Hangzhou310000 China NARI-TECH Nanjing Control Systems Ltd Jiangsu Nanjing210000 China

ISBN: (纸本)9781510666351

UHV transmission lines have problems such as high impedance and low loss. This paper mainly introduces the monitoring method of UHV transmission and distribution line based on satellite remote sensing technology, aiming at avoiding or reducing property losses caused by line problems and maintaining people's normal life. In this paper, the experimental system construction and quantitative analysis methods are mainly used, and the results of two kinds of line detection are compared by comparison. The experimental data shows that in different types of fault lines, the test time of fault is within 0.1s. © 2023 SPIE.

关键词： Electric lines

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A Sidelobe-Aware Semi-Deformable Convolutional Ship Detection Network for Synthetic Aperture Radar imagery 7th

A Sidelobe-Aware Semi-Deformable Convolutional Ship Detectio...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Luo, Hao Lin, Xianming Xiamen Univ Minist Educ Key Lab Multimedia Trusted Percept & Efficient Co Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Nanjing Marine Radar Inst Nanjing 211153 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

This paper focus on SAR ship detection, which has been widely used in tasks such as marine traffic, fisheries management, battlefield posture assessment and military target reconnaissance. One popular solution is to utilise deep learning algorithms in conjunction with SAR image object detection. However, due to the unique imaging characteristics of SAR, existing solutions usually do not distinguish well between ships and interference targets in complex backgrounds. In this paper, we address this problem by proposing a sidelobe-aware semi-deformable convolution that takes full advantage of the combination of both standard and deformable convolution to learn high-quality ship features without significantly increasing the computational complexity. Specifically, it makes the feature extraction location more closely fit to the ship shape, strengthens the extraction capability of the region of the ship target itself and the sidelobe features, while reducing the extraction of background information. The channel attention mechanism is then proposed to enhance the ship local detail information extraction. Experiments on two widely used datasets show that the proposed method outperforms the state-of-the-art methods, which is effective and efficient to improve SAR ship detection.

关键词： Deformable Convolution Synthetic Aperture Radar (SAR) imagery Ship Detection remote sensing image processing

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Research on image recognition Technology of Overhead Transmission Line Damage

Research on Image Recognition Technology of Overhead Transmi...

引用

2024 International conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Zhu, Qianxin Fang, Ming Wang, Qianjin Yang, Ten Yin, Hongjiang Zhang, Yi Zhang, Bingzhen Wang, Yaoping Dang, Junpeng Wang, Tao Yunnan Power Grid Co. Ltd. Yuxi Power Supply Bureau Yuxi China

ISBN: (纸本)9781510680012

The transmission line conductors are an essential component of power system operation. Due to prolonged exposure to the external environment, these conductors are vulnerable to damage caused by various factors, leading to power system failures that have significant impacts on production and daily life. To monitor the condition of transmission conductors, assess the extent of damage, and ensure stable and safe grid operation, we propose an intelligent wire damage identification technology based on transmission wire damage images. Initially, we apply gray variance normalization and median filtering techniques to enhance the quality of the transmission wire image. Subsequently, local threshold segmentation is performed on the enhanced image to extract the wire area. Leveraging the characteristic that damaged areas exhibit higher gray values compared to other regions, we employ a gray value vertical and horizontal projection algorithm along with subsequent filtering processes for identifying surface damages on wires. This approach effectively transforms wire damage recognition into a data processing problem. Finally, through a series of experiments conducted in power maintenance operations, we validate its feasibility. © 2024 SPIE.

关键词： Damage detection

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Contextual Information Classification of remotely Sensed images 6

Contextual Information Classification of Remotely Sensed Ima...

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6th International conference on pattern recognition and image Analysis (IPRIA)

作者： Dori, Alirza Ghassemian, Hassan Imani, Maryam TarbiatModares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350314557

This work proposes a multidisciplinary contextual information extraction and decision fusion approach for increasing the classification accuracy. It improves the image classification with integrating the results of various classifiers. The proposed method is implemented in three-steps: 1) contextual feature extraction using four different feature extractors methods: a) Gray Level Cooccurrence Matrix,b) Gabor filters, c) Laplacian Gaussian filters and d) Gaussian Derivatives Functions;2) classification of contextual features using four different classification rules (ML, Tree, KNN and SVM) by using only 2% of data for training the classifiers;and 3) finally, decision fusion using six decision fusion rules. The experimental results on real remotely sensed images have been presented.

关键词： contextual information feature extraction classification decision fusion remote sensing

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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...

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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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