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7th International Conference on Computer, Communication and Signal processing, ICCCSP 2023

ISBN: (纸本)9783031398100

The proceedings contain 26 papers. The special focus in this conference is on Computer, Communication, and Signal processing. The topics include: Forecast of Movie Sentiment Based on Multi Label Text Classification on Rotten Tomatoes Using Multiple Machine and Deep Learning Technique;an Ensemble Approach to Hostility Detection in Hindi Tweets;an Optimized Framework for Diabetes Mellitus Diagnosis Using Grid Search Based Support Vector Machine;inquisition of Vision Transformer for Content Based Satellite image Retrieval;impact of Spectral Domain Features for Small Object Detection in remote sensing;application of Phonocardiogram and Electrocardiogram Signal Features in Cardiovascular Abnormality recognition;analyzing Cricket Biomechanical Parameters Through Keypoint Detection and Tracking;multi-feature Based Sea–land Segmentation for Multi-spectral and Panchromatic remote-sensing imagery;deoxyribonucleic Acid Cryptography Based Least Significant Byte Steganography;detection and Estimation of Diameter of Retinal Vessels;a Simple Hybrid Local Search Algorithm for Solving Optimization Problems;Performance Study of RIS Assisted NOMA Based Wireless Network with Passive IoT Communication;multi-channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks and Their Attack Signatures;a Study in Analysing the Critical Determinants of Internet of Things (IoT) Based Smart processing for Sustainable Supply Chain Management;IOT Enabled Rover for remote Survey of Archeological Areas;an Embedded System for Smart Farming Using Machine Learning Approaches;an Intrusion Detection System for Securing IoT Based Sensor Networks from Routing Attacks;automated Summarization of Gastrointestinal Endoscopy Video;interpretation of Feature Contribution Towards Diagnosis of Diabetic Retinopathy from Exudates in Retinal images;false Positive Reduction in Mammographic Mass Detection;Collaborative CNN with Multiple Tuning for Automated Coral Reef Classification.

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An Improved YOLOv8 Algorithm for Rotating Object Detection in Unmanned Aerial Vehicle remote sensing images

An Improved YOLOv8 Algorithm for Rotating Object Detection i...

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pattern recognition and Artificial Intelligence (PRAI), International Conference on

作者： Hongqing Song Juntao Pu Bin Zhang Yu Chen Rongzhong Baima Keyan Zhu Jiajia Ning Civil Aviation Logistics Technology Co. Ltd Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Unmanned aerial vehicle remote sensing images suffer from problems such as arbitrary object orientation and dense arrangement of small targets, which makes horizontal box object detection difficult. To address these issues, an improved YOLOv8 based remote sensing image rotating object detection algorithm (R-YOLOv8) is proposed. Firstly, the long edge definition method of the five-parameter method is used to represent the rotation box, which allows the bounding box to cover the small target at any angle. This can better fit the actual shape and arrangement of the target, which effectively solves the problem of accidental deletion caused by inaccurate horizontal box positioning. Then, ProbIOU Loss is introduced as the regression loss function to convert the rotation box into a two-dimensional Gaussian distribution, which solves the problem of angle periodicity caused by the representation of rotating targets. Finally, the BiFormer attention mechanism is introduced to improve the accuracy of target detection while reducing parameters and computational complexity. This paper conducts relevant experiments on the UAV-ROD dataset. Compared to classical networks and existing oriented object detection networks, the R-YOLOv8 algorithm performs better in the case of a model size of only 2.8 MB, mAP@0.5 reaches 98.7%, which verifies the effectiveness and progressiveness of the proposed method.

关键词： Accuracy Attention mechanisms Shape image edge detection Imaging Object detection Gaussian distribution Autonomous aerial vehicles pattern recognition remote sensing

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Local Representation Learning Using Visual Priors for remote sensing

Local Representation Learning Using Visual Priors for Remote...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Viktor Brack Dominik Koßmann Pattern Recognition Group TU Dortmund University

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Recent self-supervised methods for learning image representations on natural images have shown different approaches to tackle either classification or dense prediction tasks. By utilizing global features per image and a set of local features, they try to tackle the trade-off between these downstream tasks. Unfortunately, remote sensing imagery is seldom comprised of only one central object, but of many different regions with varying types of region boundaries. Thus, many prominent approaches for natural images do not model this inside their local feature learning schemes. We utilize the benefits of local region masks as a prior to representation learning through local feature matching. On dense remote sensing tasks, like semantic segmentation for land-cover mapping our approach yields better results than the current matching schemes and is on par given the trade-off between local and global features for the largest current remote sensing benchmarks with multiple tasks.

关键词： Representation learning Visualization Semantic segmentation Vegetation mapping Land surface image representation Benchmark testing

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International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

International Conference on Signal Processing, Computer Netw...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

ISBN: (纸本)9781510664616

The proceedings contain 101 papers. The topics discussed include: research on the demodulation method of tracking angular error signal acquired by relay star for spread spectrum users;a novel authentication scheme for PIoT based on aggregate signature;small sample underwater acoustic signal modulation classification and recognition based on ensemble learning;doubly robust joint learning for factorization machine on data missing not at random;design of an adaptive fractional order image denoising model based on image complexity;study on system design of radar for LSS-target (the low altitude, slow speed, and small target) based on trajectory prediction;study on influencing factors of atherosclerotic plaque characteristics based on numerical simulation;a review of enabling tactile rendering on multimedia terminals;research on event trigger word extraction based on semantic enhancement;adaptive method of process flow parameter values setting based on remote sensing image metadata;and trajectory visualization based on multi-level spatio-temporal data organization model.

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MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free remote sensing imagery

arXiv

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arXiv 2025年

作者： Li, Yansheng Wu, Yuning Cheng, Gong Tao, Chao Dang, Bo Wang, Yu Zhang, Jiahao Zhang, Chuge Liu, Yiting Tang, Xu Ma, Jiayi Zhang, Yongjun Information and computing science from Shandong University Weihai China Pattern recognition and intelligent system from the Huazhong University of Science and Technology Wuhan China Computer science and technology from Wuhan University Wuhan China Xidian University Xi’an China Pattern recognition and intelligent systems from Northwestern Polytechnical University Xi’an China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China Institute for Pattern Recognition and Artificial Intelligence Huazhong China University of Science and Technology Remote sensing science and technology from Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Electronic circuit and system from Xidian University Xi’an China University of Colorado at Boulder BoulderCO United States Control science and engineering from the Huazhong University of Science and Technology Wuhan China Geodesy and photography from Wuhan University Wuhan China

Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples. This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios. To address this limitation, we introduce the Million-scale finE-grained geospatial scEne classification dataseT (MEET), which contains over 1.03 million zoom-free remote sensing scene samples, manually annotated into 80 fine-grained categories. In MEET, each scene sample follows a scene-in-scene layout, where the central scene serves as the reference, and auxiliary scenes provide crucial spatial context for fine-grained classification. Moreover, to tackle the emerging challenge of scene-in-scene classification, we present the Context-Aware Transformer (CAT), a model specifically designed for this task, which adaptively fuses spatial context to accurately classify the scene samples. CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes. Based on MEET, we establish a comprehensive benchmark for fine-grained geospatial scene classification, evaluating CAT against 11 competitive baselines. The results demonstrate that CAT significantly outperforms these baselines, achieving a 1.88% higher balanced accuracy (BA) with the Swin-Large backbone, and a notable 7.87% improvement with the Swin-Huge backbone. Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping. The source code and dataset will be publicly available at https://***/project/***. Copyright © 2025, The Authors. All rights reserved.

关键词： image classification

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remote sensing Small Target Detection Research Based on Improved YOLOv7

Remote Sensing Small Target Detection Research Based on Impr...

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Signal processing and Intelligent Computing (SPIC), International Conference on

作者： Jia Guo Xiannan Wang Meihua Song Shuang Zhao Cean Guo Jian Zhang Hao Cheng College of Equipment Engineering Shenyang Ligong University Shenyang China Key Laboratory of Safe Mining of Deep Metal Mines Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

This study introduces a streamlined improvement algorithm based on YOLOv7 to address the challenges of multiple small targets, complex backgrounds, limited feature points, and environmental influences in remote sensing images. The algorithm incorporates the GSConv module and upsampling to achieve more effective feature fusion. The GSConv module combined with the VoV-GSCSP module achieves Slim-Neck for reducing algorithm complexity and improving accuracy. The Receptive Field Enhancement (RFE) module is introduced after the spatial pyramid pooling layer (SPPCSPC) to enhance the receptive field of small targets. By changing the settings of convolution operations and increasing the receptive field size of each convolutional layer, local and global features in the image are better captured. Information flow is maintained through skip connections. Experimental findings show that the enhanced YOLOv7 algorithm reaches an mAP of 95.5% on the NWPU VHR-10 dataset, marking a 6.4% improvement over the original algorithm’s mAP. That improves the accuracy of object detection in remote sensing images.

关键词： Location awareness Accuracy image recognition Target recognition Convolution Signal processing algorithms Object detection Streaming media Complexity theory remote sensing

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基于深度学习的遥感图像舰船目标检测算法综述

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光学精密工程 2023年第15期31卷 2295-2318页

作者：黄泽贤吴凡路傅瑶张雨姜肖楠中国科学院长春光学精密机械与物理研究所吉林长春130033 中国科学院大学北京100049

海面舰船目标检测是遥感图像处理和模式识别领域备受关注的重点研究方向,对舰船目标的自动检测在民用和军用方面都具有重大意义。梳理和分析了典型基于深度学习的目标检测算法的优缺点,并进行了对比和总结;归纳了基于深度学习的舰船目... 详细信息

海面舰船目标检测是遥感图像处理和模式识别领域备受关注的重点研究方向,对舰船目标的自动检测在民用和军用方面都具有重大意义。梳理和分析了典型基于深度学习的目标检测算法的优缺点,并进行了对比和总结;归纳了基于深度学习的舰船目标检测的技术现状,并从多尺度检测、多角度检测、小目标检测、模型轻量化和大幅宽遥感图像舰船目标检测等方面对技术现状进行了详细的介绍。最后,介绍了舰船目标识别算法常用的评价标准和现有的舰船图像数据集,探讨了遥感图像舰船目标检测算法现在所面临的问题和未来的发展趋势。

关键词：遥感图像舰船目标检测卷积神经网络图像数据集

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5th International Conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies—Mul...

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5th International Conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751808

The proceedings contain 59 papers. The special focus in this conference is on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning. The topics include: Digital Twin Technology Approach Based on the Hierarchical IDP Tensor Decomposition;modeling Technology for Complex Dynamic Operating Environment of Power Grid Based on Digital Twins;design of a Digital Twin Platform Based on Distributed Computing and Resource Optimization Algorithms;deep Learning Network Optimization Combining 3D Imaging and Multidimensional Signal processing;time Series Prediction Application of Deep Learning in Multidimensional Signal processing;improving Abstractive Summarization with Graph Sequence Model;advancing Semantic Segmentation and Interpretation of 3D images Through Integrated Deep Learning and Natural Language processing Techniques;children’s Toy Product Design Based on Augmented Reality Technology;based on the Neural Network Classification of Human Behavior Research;development of an Independent Adversarial Sample Detection Model, Based on image Features;Visualization and Analysis of CNN Adversarial Training;video Violence Detection Method Based on Multi-Feature and Graph Convolutional Network;Enhancing Realized Volatility Prediction: An Exploration into LightGBM Baseline Models;terminal Anomaly Discovery Technology Based on Service Behavior Deviation;An Efficient CRNN Model with the Multi-scale Feature Fusion for Text recognition from Chinese Medical Reports;advancing Precision Drug Screening: Integrating Imaging Technology and Artificial Intelligence for Novel Models;a Short Video Retrieval Method Based on Spatio-Temporal Feature Fusion;spatial Data Transformation and Fusion Expression Based on Geometric Algebra;EM2-YOLO: Lightweight remote sensing image Detection;Tower-Type Detection of UAV Aerial image Based on YOLOV5 Network Model.

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Hierarchical fusion convolutional neural networks for SAR image

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pattern recognition LETTERS 2021年 147卷 115-123页

作者： Jiang, Yinyin Li, Ming Zhang, Peng Tan, Xiaofeng Song, Wanying Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Ctr Informat Sensing & Understanding Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Remote Sensing Image Proc & Fus Grp Xian 710071 Peoples R China

Convolutional neural network (CNN) has achieved promising results in image segmentation recently. However, for the segmentation of synthetic aperture radar (SAR) images with complicated scene, the single receptive field in CNN has a limited ability to effectively capture structural and regional information at the same time. In this paper, we propose a hierarchical fusion CNN (HIFCNN) model for SAR image segmentation. At each convolutional layer, HIFCNN sets several different-sized receptive fields, and thus extracts hierarchical features. Concretely, the larger-sized receptive field captures regional information and is robust against speckle, while the smaller one preserves the structural information well. Then, based on the Dempster-Shafer evidential theory, the proposed hierarchical network, HIFCNN, implements a decision-level fusion to integrate these hierarchical features. In this way, the structural and regional information can be accurately captured by different receptive fields, which is beneficial for edge location, structure preservation and region homogeneity in SAR image segmentation. The effectiveness of HIFCNN model is demonstrated by the application to the segmentation of the simulated images and real SAR images. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Synthetic aperture radar image segmentation Hierarchical fusion convolutional neural networks Dempster-Shafer evidential theory

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad image processing

Multi-Plant Leaf Disease Detection and Classification using ...

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Recent Advances in Science and Engineering Technology (ICRASET), International Conference on

作者： S S Aishwarya Pushpa Ravikumar I V Chaithra Deeksha Sagar Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA Department of Computer Science and Engineering AIT Adichunchanagiri Institute of Technology Chikmagalur Karnataka INDIA

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Every nation’s basic need is the agricultural products. Disease-ridden leaves have an effect on the nation’s agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are $\mathbf{2 0 6 3 6}$ photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, $\mathbf{1 2}$ are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing; specifically, we achieved $\mathbf{9 8. 2 9 \%}$ accuracy in training and $\mathbf{9 8. 0 2 9 \%}$ accuracy in testing across all utilized data sets. pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision.

关键词： Training Accuracy Neural networks Classification algorithms pattern recognition Convolutional neural networks remote sensing Diseases Testing Principal component analysis

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