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

共 707 条记录，以下是21-30 订阅

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Detection and Classification of Satellite remote sensing images Using Hybrid Segmentation and Feature Extraction with Effective Algorithms 2

Detection and Classification of Satellite Remote Sensing Ima...

引用

2nd International conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Vinuja, G. Devi, N. Bharatha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Cse Chennai India

ISBN: (纸本)9798350382952

The remote sensing image analysis, classification, and pattern recognition processes all depend on image segmentation. In this research, a search-based convolutional neural network (SBCNN) is used to identification method for remote sensing images. Prior to applying the image data to the SKFCM with PeSOA segmentation step, the image data must first undergo pre-processing. When pre-processing satellite images for road networks, noise is removed using an improved median filtering technique. The image is then segmented using the SKFCM with PeSOA Segmentation technique to have inverse shape determination with lowest energy usage. Using an intensity constraint, it is possible to identify the segments of a building and vegetation, a road, and a barren area of land. Following segmentation, MLBP with DWT feature extraction is performed on the road satellite images, and the SBCNN is then used to categorize the images. After associated with obtainable methods, the findings of the suggested technique display excellent precision of 98.6%. © 2024 IEEE.

关键词： Feature extraction

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Cultivated land segmentation in RGB remote sensing images: nonuniform regularization with kernel space and graph cut

Cultivated land segmentation in RGB remote sensing images: n...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Wu, Wangsheng College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

To improve the application efficiency of RGB remote sensing images in agricultural land resource surveys, a cultivated land segmentation algorithm based on kernel space non-uniform regularization classification and improved graph cut was proposed. Firstly, extracting texture and color features of remote sensing images using Local Binary pattern algorithm (LBP), Gabor filters, and RGB, HSV color space, respectively. Next, introducing a kernel method to map data from low-dimension to high-dimension, and construct a kernel space-based non-uniform regularization sparse representation model to classify and segment images in pixel level. Finally, an innovative graph cut algorithm is enhanced by incorporating a Gaussian distribution to redefine the penalty term for homogeneous regions and introducing a new color gradient measure to define the penalty term for boundaries. This approach effectively removes scatter and restricts the segmentation boundary. The average classification accuracy and average F1 score of the classifier proposed in this paper are about 2% and 3% higher than those of recent regularized subspace classifiers, respectively. Compared with the Graph cut algorithm, the proposed improved graph cut algorithm has an average mIoU improvement of about 9%. The average accuracy of the whole segmentation algorithm is 95.43%, and the average mIoU is 88.56%. Compared with the comparison algorithm, the proposed algorithm has higher segmentation accuracy, which proves that the proposed algorithm can adapt to the cultivated land segmentation scene of remote sensing images and is effective. © 2024 SPIE.

关键词： RGB color model

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Improved remote sensing image Target Detection Algorithm Based on YOLOv5 7

Improved Remote Sensing Image Target Detection Algorithm Bas...

引用

7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Xiaodi Xu, Yan Liu, Guorong Hu, Ping Chen, Zhaoyuan Li, Fang College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350350890

remote sensing information processing plays an important role in many fields such as environmental protection, urban planning, military reconnaissance and so on. However, there are problems in remote sensing images such as complex background, large size change, small target, etc. A remote sensing image target detection algorithm to improve YOLOv5 is proposed. Using a certain percentage of data enhancement technology at the input side to enrich the data features and improve the model generalization ability and robustness;for the problem of small targets in remote sensing images, SPD-Conv module is added to reduce the loss of fine-grained information and improve the detection performance of small targets;the design uses the DCNv2 module to replace part of the neck structure of the C3 layer, which breaks through the limitation of traditional convolution and improves the feature extraction ability of the network in remote sensing images. Experimental results show that the improved YOLOv5 model improves the detection accuracy by 3.9%. © 2024 IEEE.

关键词： Target tracking

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Adaptive Cross-Spatial sensing Network for Change Detection 7th

Adaptive Cross-Spatial Sensing Network for Change Detection

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Jin, Liyuan Zhang, Yanduo Lu, Tao Wang, Jiaming Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Peoples R China Hubei Univ Arts & Sci Xiangyang 441053 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

The problem of unbalanced category distribution of foreground and background in remote sensing change detection (CD) tasks has become a key problem in improving the performance of CD models when processing complex remote sensing image data. To address this issue, we develop a novel adaptive cross-spatial sensing network (ACS-Net), aiming to dynamically adjust the degree of attention of the foreground and background to achieve accurate and efficient CD. The proposed ACS-Net combines the global context module (GCM) and the cross-spatial learning module (CSLM) by introducing the adaptive weight adjustment mechanism (AWAM) to adaptively adjust the weight coefficients of the GCM and CSLM, thereby enhancing the ability to extract foreground features. GCM captures the global context information of the image to generate global perceptual features, thereby helping to distinguish the foreground and background. While CSLM focuses on learning the information association between different spatial locations, which can enhance the model's sensitivity to spatially changing details, and then focus on local feature extraction of foreground. The experimental results show that the F1-Score index of ACS-Net on two public datasets (Levir-CD and Lebedev-CD) reaches 91.53% and 96.25% respectively, validating its effectiveness and superiority.

关键词： Change detection Deep learning remote sensing High-resolution images

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An enhanced feature matching multi-temporal port remote sensing image registration network E-SuperGlue

An enhanced feature matching multi-temporal port remote sens...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： He, Xinyu Feng, Shuyi Shao, Wenbo He, Hengxiang Wu, Liming Shanghai Aerospace Electronic Technology Institute Department of Aerospace Electronic Research Shanghai China

ISBN: (纸本)9781510680425

Multi-temporal collaborative analysis of port scenes can enhance the representation ability of image scenes, and image registration is required before multi-temporal analysis. In this paper, an image registration network E-SuperGlue with enhanced feature matching is proposed to solve the problems such as the difficulty of extracting feature points and matching feature descriptors for port multi-temporal image registration. Our network takes SuperGlue network as the basic framework. Firstly, Focus is introduced into the feature extraction network to increase the number to increase the number and detection rate of feature points. Secondly, LFPE module is added to feature matching network coding module to improve the information efficiency of feature descriptor coding. Finally, an improved multi-layer sensing structure E-MLP is added to the feature matching network to improve the utilization rate of channel information. © 2024 SPIE.

关键词： Graph neural networks

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Novel algorithm development for event-based sensing deployment 35

Novel algorithm development for event-based sensing deployme...

引用

conference on pattern recognition and Prediction XXXV

作者： Kjellstrand, C. Bjorn Casias, Lilian K. Hagopian, Kaylin Pattyn, Christian Saltonstall, Christopher Shank, Joshua Sandia Natl Labs 1515 Eubank Dr Albuquerque NM 87123 USA

ISBN: (纸本)9781510673991;9781510673984

Event-based sensors (EBS) consist of a pixelated focal plane array in which each pixel is an independent asynchronous change detector. The analog asynchronous array is read by a synchronous digital readout and written to disk. As a result, EBS pixels consume minimal power and bandwidth unless the scene changes. Furthermore, the change detectors have a very large dynamic range (similar to 120 dB) and rapid response time (similar to 20 us). A framing camera with comparable speed requires similar to 3 orders of magnitude more power and similar to 2 orders of magnitude higher bandwidth. These features make EBS an appealing technology for proliferation detection applications. remote sensing deployed in the field requires low power, low bandwidth, and low complexity algorithms. EBS inherently allows for low power and low bandwidth, but a drawback of event-based sensors is the lack of mature image analysis algorithms. While analysis of conventional imagers draws from decades of image processing algorithms, EBS data is a fundamentally different format;a series of x, y, asynchronous time, and polarization change (increase/decrease) as opposed to x, y, and intensity at a regularly sampled framerate. To leverage the advantages of EBS over conventional imagers, our team has worked to develop and refine image processing algorithms that use EBS data directly. We will discuss these efforts, including frequency and phase detection. We will also discuss the field applications of these algorithms such as degraded visual environments (e.g., fog) and defeating laser dazzling attempts.

关键词： Event-based sensing EBS algorithm remote sensing degraded visual environment laser dazzling

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Monitoring Technology and Data processing Methods for Chinese Bridges Research Progress

Monitoring Technology and Data Processing Methods for Chines...

引用

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

作者： Chen, Xiaonian Chen, Cheng College of Civil Engineering Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9781510680012

Since the number of bridges in China keeps increasing and some bridges with the damage problems brought by the age of are gradually taken into account, thus the health monitoring of bridges becomes much more important. Based on the research of China's intelligent sensing materials in the past decade, this paper summarizes the function of common sensing devices in crack pattern recognition, rust expansion monitoring, damage identification, curing shrinkage strain, and fatigue damage monitoring, and clarifies the feasibility of new monitoring technologies in practical engineering. It is also cited that the advantages and working principles of two data treatment, wavelet analysis and Long-term and short-term memory neural network, applied in the field of bridge health monitoring. The results show that intelligent sensing material and signal analysis technology has a wide application prospect in the future bridge surveillance work because of its good working performance. This paper can provide some reference for the application of intelligent sensing and signal analysis technology in the information of bridge health monitoring. © 2024 SPIE.

关键词： Wavelet analysis

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UAV remote sensing imaging technology applied to agricultural plant protection

UAV remote sensing imaging technology applied to agricultura...

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

作者： Liu, Ying Nanjing Vocational Institute of Transport Technology China

ISBN: (纸本)9781510666351

This paper addressed the contradiction between the decrease of labor force and the increase of agricultural output demand in agricultural development, combined with the technical advantages of UAV imaging radar in SAR imaging, ground moving target detection and height measurement, analyzed the key technology of UAV imaging radar applied to agricultural plant protection. The signal processing scheme and processing process of UAV imaging radar for agricultural plant protection were designed, and the experimental verification results using the measured data were reasonable and feasible. The verification results of the measured data showed that the UAV remote sensing imaging technology can realize the high-resolution imaging and preliminary ground object classification in the operation area. © 2023 SPIE.

关键词： Radar imaging

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Analysis of spectral characteristics of typical construction waste

Analysis of spectral characteristics of typical construction...

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2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Zhang, Jiayuan College of Geomatics and Geoinformation Guilin University of Technology Guilin China

ISBN: (纸本)9781510680425

With the acceleration of urban construction and renovation, the volume of construction waste has significantly increased, accounting for 30% to 40% of the total urban solid waste emissions. Therefore, analyzing the spectral characteristics of typical construction waste is crucial for remote sensing identification and monitoring, providing essential ground verification information. This study conducted spectral data collection of construction waste in areas such as Daxing District of Beijing and Pingdingshan City, Henan. Various spectral analysis methods were applied to examine its characteristics. The findings revealed that: (1) the wavelength range is between 1913nm and 1932nm, the spectral curves of all kinds of construction wastes have obvious reflection troughs, In the range of 1660-1690nm, organic building materials exhibit absorption valleys. (2) In the wavelength intervals of 600~1100nm and 1860~1930nm, spectral curves exhibit consistent trends, rendering them unsuitable for discrimination. (3) In the range of 1900~2200nm, samples exhibit reduced susceptibility to the spectral characteristics of the dust-proof net, closely mirroring the spectral features of uncovered samples, thereby rendering it suitable as a basis for classification. © 2024 SPIE.

关键词： Reflectivity Absorption Reflection remote sensing Visible radiation Data acquisition Smoothing Spectrometers image classification Solids

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Research on Regional Water Resources Classification Algorithm Based on Improved Deep Residual Network 6

Research on Regional Water Resources Classification Algorith...

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6th IEEE International conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Xu, Yan Tang, Binhui Chengdu Jincheng College Chengdu China

ISBN: (纸本)9798350325485

In order to realize the rapid and accurate classification of regional water resources, this paper designs the regional water resources classification algorithm based on the improved deep residual network. This paper analyzes the overall situation of regional water resources distribution in China, and takes the remote sensing image of Gaf-2 satellite as the basis of algorithm design, and preprocesses the remote sensing image of regional water resources, such as enhancement, marking and segmentation. On this basis, the pyramid network is used to improve the depth residual network, and the improved depth residual network is combined with the pre-processing results of remote sensing image to obtain the regional water resources classification results. The experimental results show that the proposed algorithm has short preprocessing time, low classification error rate, short classification time and better practical application effect. © 2023 IEEE.

关键词： remote sensing

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