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Research on speckle image reconstruction method based on deep learning 24

Research on speckle image reconstruction method based on dee...

引用

2024 International conference on Virtual Reality, image and signal processing, ICVISP 2024

作者： Li, Xuandong Chen, Musheng College of physics and information engineering Quanzhou Normal University Fuzhou China College of Photonic and Electronic Engineer Fujian Normal University Fuzhou China

ISBN: (纸本)9798400710926

Optical speckle image reconstruction is crucial in fields such as biomedical imaging, optical coherence tomography, and optical remote sensing. However, speckle images often suffer from noise and scattering, leading to diminished image quality. This paper introduces a deep learning-based approach to enhance the accuracy and quality of reconstructed speckle images. The process begins with the construction of an experimental optical setup using a spatial light modulator to generate the corresponding speckle patterns. A novel network model, named pix2pix, is then developed based on the generative adversarial network (GAN), integrating both generator and discriminator modules. The model is trained using both the collected speckle image data and real data. Extensive experiments have validated the effectiveness of our approach, showing significant improvements in reconstruction accuracy and computational efficiency. The results indicate that, compared to traditional methods, the deep learning-based technique greatly enhances image clarity, effectively reduces noise, and demonstrates superior generalization in image reconstruction. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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A KEY FEATURE-ENHANCED NETWORK FOR remote sensing OBJECT DETECTION 30

A KEY FEATURE-ENHANCED NETWORK FOR REMOTE SENSING OBJECT DET...

引用

30th IEEE International conference on image processing (ICIP)

作者： Liu, Yundong Dong, Yan Kang, Haonan Gao, Guangshuai Li, Chunlei Liu, Zhoufeng Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China Natl Univ Singapore Dept Stat & Data Sci Singapore Singapore

ISBN: (纸本)9781728198354

Currently, remote sensing object detection methods based on deep learning have been widely used in military investigation, ocean monitoring, urban planning and post-disaster relief. However, many difficulties, such as complex backgrounds, indistinguishable object appearances, large-scale variations, and non-uniform distributions, limit the accuracy of detectors. To deal with these problems, a key feature-enhanced network (KFENet) is proposed to improve the detection accuracy. Specifically, a multi-scale feature fusion (MSFF) module is given to obtain richer feature expression, which can explore inherent structural information of images at different scales. Next, an effective activation detection head (EAD-Head) is designed to adaptively capture key features required for classification and regression tasks, respectively. Finally, we implement our strategy within the framework of YOLOX. Experiments on DIOR and RSOD datasets show that the proposed algorithm outperforms state of the art methods.

关键词： object detection remote sensing images multi-scale feature fusion effective activation detection head

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Machine Learning Approach for Automatic Weld Defect Detection Using Radiographic images 2

Machine Learning Approach for Automatic Weld Defect Detectio...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Edupuganti, Venkata Ramana Penekalapati, Sai Varun Namala, Kiran Kumar VNR Vignana Jyothi Institute of Engineering and Technology Mechanical Engineering Department Hyderabad India

ISBN: (纸本)9798350366570

Radiographic images of weld beads are important to inspect subsurface weld defects for safety assurance. The diversified problems associated with the human analysis of radiographic images of the weldments paved the way to the use of machine learning approach for the detection of subsurface defects. Automatic weld defect inspection involves image pre-processing, defect detection, and classification of defects. The techniques of digitization of radiographs, image pre-processing, defect segmentation and classification are summarized. Advanced methods for radiographic weld defect image classification using machine learning and challenges associated with the current technology are discussed for future improvement. © 2024 IEEE.

关键词： image segmentation

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Evaluation of Very-high Resolution Satellite image Compression to Support Agriculture Monitoring

Evaluation of Very-high Resolution Satellite Image Compressi...

引用

IEEE International conference on Aerospace Electronics and remote sensing Technology (ICARES)

作者： Candra, Danang S. Ulfa, Kurnia Rasyidy, Fadillah H. Hestrio, Yohanes Fridolin Prabowo, Yudhi Rahayu, Mulia I. Natl Res & Innovat Agcy BRIN Res Ctr Remote Sensing Cibinong Indonesia Natl Res & Innovat Agcy BRIN Res Ctr Hydrodynam Technol Surabaya Indonesia Natl Res & Innovat Agcy BRIN Ctr Data & Informat Cibinong Indonesia

ISBN: (纸本)9798350344004

Recently, the need for detailed images such as very high-resolution satellite imagery has been increasing, but there is an issue of heavy processing for the image as the size of the file is very big. To address this issue, this study aims to evaluate the image compression method in terms of decreasing the size of the imagery. To assess the reliability of the method in preserving the quality of the compressed image, Structural Similarity Index, Peak signal-To-Noise Ratio, and Universal Quality Index were applied to the compressed image. The results show that the compressed image successfully decreased up to 95% in the quality value of 5 and the method can preserve the quality of the compressed images very well.

关键词： image compression discrete wavelet transform Pleiades Neo JPEG2000 SSIM PSNR UQI

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Unsupervised Multiclass Change Detection and Mapping Using Deep Neural Network 7

Unsupervised Multiclass Change Detection and Mapping Using D...

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IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Kheddam, Radja Tahraoui, Ahmed USTHB Fac Elect Engn Image Proc & Radiat Lab Algiers Algeria

ISBN: (纸本)9798350351491;9798350351484

This work deals with the task of land use and land cover (LULC) change detection using multi-temporal multispectral remote sensing images. In the last few years, deep learning-based change detection methods have been successfully implemented for automatic LULC change detection. Although these elaborated methodologies achieved a very high score in the detection accuracy, they are able to provide only binary change mapping where are illustrated change regions and non-change regions without any specification of the ground classes nature. In order to get a multiclass change detection leading to a more accurate change mapping, we proposed in this paper a fully unsupervised three steps methodology where during the first step, a binary change mapping is obtained using k-MAD (kernel multivariate alteration detection) components combined with a Chi-2 test thresholding, during the second step, the change and non-change regions are iteratively classified due to the AP (affinity propagation) clustering algorithm to reach multiclass non-change area and "from-to" change area, finally in the third step, samples from the changed and unchanged classes are involved in a DNN (deep neural network) architecture to provide a multiclass land change mapping. Co-registered bi-temporal multispectral images taken over the northeastern region of Algiers, Algeria, between 1997 and 2001 by the American LANDSAT-TM satellite are used to verify the effectiveness of the proposed scheme. The obtained "from to" land change map validated by means of the spectral signature analysis is more informative on the change and non-change pixels comparing to the binary land change map.

关键词： Unsupervised change detection kernel MAD Affinity Propagation (AP) Deep Neural Network (DNN)

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Comparison of Pansharpening Algorithms: Developing a Data-Driven Evaluation Framework 2

Comparison of Pansharpening Algorithms: Developing a Data-Dr...

引用

2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Liu, Yu Tong, Qingwei Zhao, Zhipeng Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798331515669

Convolutional neural network (CNN)-based methods are increasingly crucial in image processing and computer vision. In remote sensing image pansharpening, CNNs have shown excellent fusion performance, attracting significant research interest. However, challenges remain, such as the lack of detailed pansharpening dataset simulations, standardized training-testing datasets, and simple, unified coding frameworks. Additionally, there is insufficient evaluation of recent deep learning pansharpening methods and evaluation of algorithms from the perspective of different subsequent applications, limiting comprehensive evaluation. This study constructs a comprehensive evaluation system for datasets, fusion algorithms, and evaluation methods. By statistically analyzing results, improve the accuracy and richness of algorithm evaluations, enabling more effective and objective method comparisons. Used WorldView-2 satellite imagery for visual assessments, supervised and unsupervised quantitative evaluations on 100 images, and land cover classification accuracy experiments. Traditional methods like BDSD and PRACS showed strong fusion results and land cover classification accuracy, deep learning methods generally outperformed traditional ones. This research provides detailed images and data resources for pansharpening, offering valuable references for future studies in this field. © 2024 IEEE.

关键词： deep learning Pansharpening quality assessment (key words) remote

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Hyperspectral image methane detection based on DETR 16

Hyperspectral image methane detection based on DETR

引用

2024 16th International conference on Graphics and image processing, ICGIP 2024

作者： Liu, Qiwen Li, Xiaoli Du, Zhenlong School of Computer Science and Technology Nanjing TECH University Nanjing211816 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

As a potent greenhouse gas, methane (CH4) leakage not only intensifies global warming, but also brings huge security risks. In recent years, with the rapid development of remote sensing technology, methane leakage detection using hyperspectral images has become a research hotspot. Among them, the airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) shows great potential in methane leakage detection with its high spectral resolution and wide spectral coverage. However, due to the complexity of land type conditions and the interference of confounding factors similar to the spectral characteristics of methane, the existing analytical methods are often difficult to obtain ideal detection results. Therefore, based on the DETR model, a novel end-to-end spectral absorption wavelength sensing transformer target detection network is proposed to improve the accuracy and efficiency of methane gas leakage detection. The reference spectrum feature generator and query refiner modules together improve the design of traditional transformer. Based on the traditional linear filter, a new spectral linear filter is proposed, which can better whiten the background distribution and amplify the methane signal by strategically selecting the relevant pixels in the spectral domain. The method presented in this paper has certain significance for improving the detection of methane gas leakage. © 2025 SPIE.

关键词： remote sensing

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Road Extraction from U2Net High Resolution remote sensing images with Attention Constraints 2

Road Extraction from U2Net High Resolution Remote Sensing Im...

引用

2nd International conference on signal processing and Intelligent Computing, SPIC 2024

作者： Zhang, Yongqi Gao, Lin Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

In order to solve the problem that Convolutional Neural Networks(CNN) have poor modeling ability for global information and may degrade performance when processing target objects at different locations or scales, this paper proposes an attention-constrained U2Net (CBU2Net) high-resolution remote sensing image path extraction method. Based on the original U2Net model, spatial and channel attention mechanisms are introduced. Network model was tested on the Massachusetts road data set, and the accuracy rate, recall rate, Intersection over Union(IoU) and F1-score reached 96.72%, 98.71%, 96.55% and 98.25%, respectively. Compared with the mainstream methods U2Net and U-Net, IoU increased by 0.04% and 0.34%, and F1-score increased by 0.03% and 0.18%, respectively. The experimental outcomes demonstrate that the introduced approach exhibits commendable efficacy, successfully enhancing the precision of road segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection of over-ground petroleum and gas pipelines from optical remote sensing images 29

Detection of over-ground petroleum and gas pipelines from op...

引用

conference on image and signal processing for remote sensing XXix

作者： Chang, Huan Bai, Lu Wang, Zhibao Wang, Mei Zhang, Ying Tao, Jinhua Chen, Liangfu Northeast Petr Univ Sch Comp & Informat Technol Daqing Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Northeast Petr Univ Bohai Rim Energy Res Inst Qinhuangdao Hebei Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781510666955;9781510666962

Petroleum and gas pipelines, comprising petroleum and gas pipes and related components, play an irreplaceable role in petroleum and gas transportation. For global economic growth, petroleum and gas are crucial natural resources. However, the pipelines often cross permafrost regions with challenging working conditions. Additionally, the potential for natural disasters raises concerns about pipeline accidents, posing a threat to pipeline operational safety. In response to the complexity of pipeline supervision and management, we choose to use remote sensing method combining deep learning-based algorithms. In this work, we build a petroleum and gas pipes dataset, which includes 1,388 remote sensing images and the study area is Russian polar regions. We trained FCN and U-Net deep learning models by using our self-built dataset for the detection of petroleum and gas pipes. Models' performances were evaluated using MIoU (Mean Intersection over Union), mean precision, mean recall to evaluate the accuracy of the model's prediction results and compared them visually with ground truth. Our results find that deep learning models can effectively learn the characteristics of pipelines and achieve ideal detection results on our dataset. The MIoU of the FCN model achieved 0.885 and the U-Net model achieved 0.894. The results demonstrate that our trained models can be used to accurately identify the petroleum and gas pipelines in remote sensing images.

关键词： Semantic Segmentation U-Net FCN petroleum and gas pipes

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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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