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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Mirbod, Majid Rezaei, Baback Najafi, Mehrnoosh Department of Industrial Management Tehran North Branch Islamic Azad University Tehran Iran Department of Management and Economics Science and Research Branch Islamic Azad University Tehran Iran

After the satellite's advent, whose jobs were to image the surface of the earth, big data of imaging data of the land surface was made available to researchers in various sciences to exploit in their field of work. So, remote sensing gradually came to the attention of researchers in different sciences, for example, geography, environmental science, civil engineering, etc. Each analyzed the visual data of the earth's surface from the perspective of their field. So, spatial change recognition issues and calculating the percentage of changes at the ground level has considered. The model presented is based on machine vision, image processing, and a fuzzy interface system to reveal features. This research is in the category of applied research, and, finally, an application will be present that can lead to the development of software such as Google Earth and can add to that as an option. Another of the advantages of this model is its easy use compared to specialized software such as Arc GIS. It is the novelty of this research. © 2022 Elsevier B.V.. All rights reserved.

关键词： remote sensing

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Swin Transformer with Multi-Scale Residual Attention for Semantic Segmentation of remote sensing images 11

Swin Transformer with Multi-Scale Residual Attention for Sem...

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11th International Conference on Computing and pattern recognition, ICCPR 2022

作者： Lin, Yuanyang Wang, Da-Han Wu, Yun Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Semantic segmentation of remote sensing images usually faces the problems of unbalanced foreground-background, large variation of object scales, and significant similarity of different classes. The FCN-based fully convolutional encoder-decoder architecture seems to have become the standard for semantic segmentation, and this architecture is also prevalent in remote sensing images. However, because of the limitations of CNN, the encoder cannot obtain global contextual information, which is extraordinarily important to the semantic segmentation of remote sensing images. By contrast, in this paper, the CNN-based encoder is replaced by Swin Transformer to obtain rich global contextual information. Besides, for the CNN-based decoder, we propose a multi-level connection module (MLCM) to fuse high-level and low-level semantic information to help feature maps obtain more semantic information and use a multi-scale upsample module (MSUM) to join the upsampling process to recover the resolution of images better to get segmentation results preferably. The experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. © 2022 ACM.

关键词： Convolution

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Improve Semantic Segmentation of High-Resolution remote sensing imagery with RS-TTA 5

Improve Semantic Segmentation of High-Resolution Remote Sens...

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5th International Conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Liu, Juncheng Liu, Guangrui Zhao, Yili Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

As deep learning has been widely used in many computer vision fields, semantic segmentation techniques based on convolutional neural networks are often used in remote sensing image problems. However, its segmentation performance still needs further improvement, especially for high separation rate remote sensing images. Many adjustment strategies for segmentation performance have been proposed, such as fully convolutional networks, residual connectivity, data augmentation, and so on. However, there are a few better and easy-to-operate adjustment strategies in the testing phase of segmentation networks. In this work, we propose a tuning strategy for high-resolution remote sensing images in the testing phase of segmentation networks, which we call RS-TTA. This method is used to improve the prediction performance of the segmentation network by simply rotating and flipping horizontally to enhance the prediction samples in the test phase and fusing the enhanced prediction results. This tuning method does not need to take into account the sample data during training or the training process of the network. It can improve the prediction performance of the segmentation network by performing a simple operation on the prediction samples, and it is also more compatible with the adjustment strategies and methods used in the training process. To evaluate this method, comparative experiments were conducted on the Potsdam dataset provided by the ISPRS competition. The experimental results show that the method can improve the prediction performance of the fully convolutional residual segmentation network on high-resolution remote sensing imagery. In addition, the test results of different combinations of models demonstrate that the adjustment effect of the method is well generalized for the task of semantic segmentation of remote sensing imagery. © 2022 IEEE.

关键词： Semantic Segmentation

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SAR image SIMULATION METHOD FOR COMPLEX TARGETS BASED ON SHADOW GEOMETRY MODEL

SAR IMAGE SIMULATION METHOD FOR COMPLEX TARGETS BASED ON SHA...

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IET International Radar Conference 2023, IRC 2023

作者： Hu, Xinchang Li, Gang Han, Jianghong qiyuanlab Beijing China Tsinghua University Beijing China

ISBN: (纸本)9781839539954

Since the synthetic aperture radar (SAR) has the advantages of strong permeability and long-range detection, target recognition based on SAR images is playing an increasingly critical role in reconnaissance and marine remote sensing applications. However, due to the high cost of acquiring real SAR images, it is hard to meet the requirements of target recognition in terms of quantity and conditional coverage. This paper presents a fast SAR image simulation method for complex targets to enlarge the dataset for SAR target recognition. In the process of simulating the SAR echo, the shadow effect between the target and the background environment along the direction of radar line-of-sight (RLOS) is considered, and the radar cross-section (RCS) data is obtained through Physical Optics (PO) method. Firstly, the surface of the established 3D model is divided into small surface elements to compute the backscattering coefficient of targets by PO method. Then, the shadow region can be determined by building the incidence angle geometry model. The speed of SAR echo simulation can be improved by fast convolution and the Deramp-Chirp Scaling (CS) algorithm is adopted for imaging processing. Experimental results demonstrate the effectiveness of the proposed method, which can generate the SAR simulated images of the specified target under different system parameters. © The Institution of Engineering & Technology 2023.

关键词： Radar cross section

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Lensless inference camera: incoherent object recognition through a thin mask with LBP map generation

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OPTICS EXPRESS 2021年第7期29卷 9758-9771页

作者： Pan, Xiuxi Nakamura, Tomoya Chen, Xiao Yamaguchi, Masahiro Sch Engn Tokyo Inst Technol Midori Ku 4259-G2-28 Nagatsuta Yokohama Kanagawa 2268502 Japan Osaka Univ Inst Sci & Ind Res 8-1 Mihogaoka Ibaraki Osaka 5670047 Japan

We propose a preliminary lensless inference camera (LLI camera) specialized for object recognition. The LLI camera performs computationally efficient data preprocessing on the optically encoded pattern through the mask, rather than performing computationally expensive image reconstruction before inference. Therefore, the LLI camera avoids expensive computation and achieves real-time inference. This work proposes a new data preprocessing approach, named local binary patterns map generation, dedicated for optically encoded pattern through the mask. This preprocessing approach greatly improves encoded pattern's robustness to local disturbances in the scene, making the LLI camera's practical application possible. The performance of the LLI camera is analyzed through optical experiments on handwritten digit recognition and gender estimation under conditions with changing illumination and a moving target. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Diffraction limit image analysis image processing image reconstruction image sensors Optical sensing

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Lightweight Edge-Guided Super-Resolution Network for remote sensing images

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IEEE Transactions on Geoscience and remote sensing 2025年

作者： Wang, Yining Huang, Zhixiong Wang, Xinying Zhang, Shaodong Liu, Shenglan Feng, Lin Dalian University of Technology School of Computer Science and Technology Dalian116024 China Dalian University of Technology School of Innovation Exper iment Dalian116024 China

Recently, deep learning-based remote sensing image super-resolution (RSISR) techniques have achieved significant progress, but challenges remain in preserving critical edge details essential for high-quality image reconstruction, These details are crucial for tasks like object recognition, change detection, and accurate analysis in remote sensing imagery. Furthermore, existing RSISR methods typically require substantial computational resources, making them unsuitable for resource-constrained edge devices. To address these challenges, we propose a novel Edge-Guided Super-Resolution Network (EGSRN). The network employs an Edge Extraction Module (Edge Net) to explicitly extract edge information from low-resolution images, combined with multi-layer Feature Extraction Modules (FEM) and an Edge Information Fusion (EIF) mechanism to progressively integrate edge and image features. This design enables precise recovery of edge details, significantly enhancing the overall visual quality of the reconstructed images. Edge-aware processing enhances visual fidelity while also improving the accuracy of downstream tasks, such as classification, object detection, and change analysis. Furthermore, the network incorporates lightweight designs such as depthwise separable convolutions and channel shuffling to effectively reduce computational demands. Comprehensive experiments were conducted on two remote sensing datasets, and the model’s parameter count and floating-point operations (FLOPs) were evaluated. Results demonstrate that the proposed method achieves an excellent balance between performance and model complexity, delivering superior super-resolution reconstruction quality while maintaining low computational costs, making it well-suited for resource-limited real-world applications. © 1980-2012 IEEE.

关键词： image enhancement

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Identification of traditional settlement landscape areas in Hainan based on unmanned aerial remote sensing imagery

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Xu, Minghui Chen, Zhanchuan School of Forestry Hainan University Hainan Haikou570228 China

To enhance the accuracy of recognizing traditional settlement landscapes in Hainan, this study introduces a landscape recognition model predicated on a full convolutional neural network (FCN). The research delineates the selection of specific types and sensors for Unmanned Aerial Vehicles (UAVs), employing UAV remote sensing technology to capture image data of Hainan's traditional settlement landscapes. The collected initial image data, laden with interference information, underwent a preprocessing step. On this foundation, the landscape recognition model for Hainan's traditional settlements was developed utilizing the FCN. The empirical results derived from deploying the model reveal the presence of ancient buildings within a 400-meter radius of the principal river. Notably, these structures predominantly span a distance ranging from 100 to 350 meters. The spatial distribution pattern of these ancient edifices notably centers around the Zong ancestral hall. Furthermore, when compared to other benchmark models, the proposed FCN model exhibits superior performance in recognizing forest, grassland, and farmland within the Hainan settlement landscape, achieving average recognition rates of 88.66% and 84.91%, respectively. This investigation underscores the significant potential for applying UAV remote sensing technology in identifying traditional settlement landscapes. It provides pivotal technical support and a reference point for the survey of forest resources and ecological monitoring, thereby enhancing the applicability and dissemination of UAV technology in landscape recognition tasks. © 2024 Minghui Xu et al., published by Sciendo.

关键词： Drones

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remote sensing images Enhancement and Object Detection Using D L Technique

Remote Sensing Images Enhancement and Object Detection Using...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Sujatha Canavoy Narahari A. B. G. N. Vijayendra Guptha B. Yashwanth Kumar P. Haritha Reddy ECE Department Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

This paper unfolds a new approach to detect items of interest in remote sensing images by enhancing them with deep learning techniques such as You Only Look Once (YOLO) architecture. Therefore, in order to improve the quality of satellite photography, attention was first paid to contrast enhancement, deblurring and general picture enhancement techniques. These improved photos were then fed into the YOLO object recognition system which enabled accurate and efficient target item localization and identification. The YOLO architecture is appropriate for applications that involve fast and precise object recognition due to its one-step detection technique. This enables real-time processing of largescale remote sensing information. Our results indicate that our method outperforms traditional methods in both object recognition and image improvement across a broad range of objects including buildings, roads and vegetation. Moreover, the proposed method has resilience under various environmental conditions signifying it is a useful tool for remote sensing application even under harsh conditions such as extreme weather conditions. In conclusion, this study advances the use of deep learning techniques in remote sensing image processing thereby enabling more effective resource management and decision making across several industries. Picture quality is often assessed using PSNR and SSIM ratios, whereas image correctness can be measured by Mean Average Precision and A verase Precision.

关键词： YOLO Deep learning Accuracy Roads Noise Buildings Vegetation mapping

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image segmentation technology and its application in digital image processing 2

Image segmentation technology and its application in digital...

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2nd IEEE Asia-Pacific Conference on image processing, Electronics and Computers, IPEC 2021

作者： Cheng, Yang Li, Bingjie Information Engineering University Zhengzhou Henan450000 China

ISBN: (纸本)9781728190181

In recent years, the level of science and technology in China has been significantly improved. Digital image processing technology is the product of the times with the rapid development of science and technology, and has been applied in many fields. Digital image processing technology includes many practical technologies, and image segmentation technology is one of them. At present, the widely used pattern recognition technology is realized by image segmentation technology. Based on this, this paper mainly studies the application of image segmentation technology in digital image processing. This paper first introduces the methods of image segmentation, including threshold segmentation, clustering segmentation and edge detection segmentation. Secondly, according to a need to extract a road image for image segmentation, and achieved very good results. Finally, this paper mainly analyzes the application of image segmentation in digital image processing. It is found that image segmentation has good application in automatic license plate recognition, biomedical engineering, remote sensing engineering and fire prevention and detection. © 2021 IEEE.

关键词： remote sensing

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Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR images

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remote sensing 2023年第17期15卷 4255-4255页

作者： Zhang, Jiaqing Lei, Jie Xie, Weiying Li, Daixun Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

The fusion of hyperspectral and LiDAR images plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, such as Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we propose an Invariant Attribute-driven Binary Bi-branch Classification (IABC) method, which is a unified network that combines a binary Convolutional Neural Network (CNN) and a GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structural representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrate the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for the accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling the real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

关键词： invariant graph convolutional network (GCN) convolutional neural network (CNN) binary quantization hyperspectral image (HSI) classification

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