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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International conference on

作者： Manoj Bhaskar Chetan Barde Prakash Ranjan Nawneet Kumar Sujit Kumar Dept. of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur Bihar India Dept. of Electrical Engineering Science Technology and Technical Education Department Government Engineering College Banka Bihar Engineering University Bihar India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

image classification is crucial in remote sensing, image analysis, and pattern recognition. Object detection and classification in satellite images are crucial for applications like marine monitoring, land-use planning, ecological research, and military operations. Satellite images provide valuable spatial and temporal data, helping address real-world challenges. However, issues like data quality, availability, and distribution complicate image analysis. Deep learning (DL) algorithms have become popular for their effectiveness in classifying satellite images, sup-porting tasks such as disaster management, land-use planning, and natural resource management. This study proposed a CNN model with batch normalization for satellite image classification. The dataset includes images categorized into cloudy regions, deserts, water bodies, and green spaces. CNN excels at extracting features like edges and textures, while Batch Normalization (BN) stabilizes training by normalizing inputs and reducing covariate shifts. Together, CNN and BN improve the efficiency and accuracy of the model. The proposed model successfully classifies satellite images, outperforming existing methods with an overall accuracy of 91%, demonstrating its superior remote sensing imaae analysis capability.

关键词： Training Accuracy Biological system modeling Feature extraction Satellite images Planning Deserts Convolutional neural networks remote sensing Batch normalization

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Edge-Guided and Cross-Scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution 27th

Edge-Guided and Cross-Scale Feature Fusion Network for Effic...

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27th International conference on pattern recognition, ICPR 2024

作者： Yang, Zhiyuan Zhang, Bo Zeng, Zhiqiang Yeo, Si Yong School of Electronic and Information Engineering Beihang University Beijing China College of Computing and Data Science Nanyang Technological University Singapore Singapore Beijing Institute of Remote Sensing Equipment Beijing China Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783031783975

In recent years, MRI super-resolution techniques have achieved great success, especially multi-contrast methods that extract texture information from reference images to guide the super-resolution reconstruction. However, current methods primarily focus on texture similarities at the same scale, neglecting cross-scale similarities that provide comprehensive information. Moreover, the misalignment between features of different scales impedes effective aggregation of information flow. To address the limitations, we propose a novel edge-guided and cross-scale feature fusion network, namely ECFNet. Specifically, we develop a pipeline consisting of the deformable convolution and the cross-attention transformer to align features of different scales. The cross-scale fusion strategy fully integrates the texture information from different scales, significantly enhancing the super-resolution. In addition, a novel structure information collaboration module is developed to guide the super-resolution reconstruction with implicit structure priors. The structure information enables the network to focus on high-frequency components of the image, resulting in sharper details. Extensive experiments on the IXI and BraTS2020 datasets demonstrate that our method achieves state-of-the-art performance compared to other multi-contrast MRI super-resolution methods, and our method is robust in terms of different super-resolution scales. Our code is available at https://***/zhiyuan-yang/Edge-Guided-Cross-Scale-MRI-Super-resolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Geospatial Time Machine: A Generative Model to Enhance Spectral-Temporal Data Resolution

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Lopes, Felipe A. Sagan, Vasit Sarkar, Supria Stylianou, Abby Esposito, Flavio St Louis Univ Remote Sensing Lab St Louis MO 63108 USA St Louis Univ Dept Comp Sci St Louis MO 63108 USA

Geospatial artificial intelligence (GeoAI) and data processing techniques have significantly advanced object detection, prediction, and classification tasks. However, the availability of machine learning-ready, labeled data for specific applications such as plant disease detection remains the major challenge for the broader adoption of GeoAI. For instance, collecting temporal unmanned aerial vehicle (UAV) imagery of agricultural crops to track disease emergence and progress requires substantial human labor and resources, which is often limited to a small spatial scale. Recognizing the pivotal role of temporal data in pattern recognition, object detection, and scene reconstruction, we introduce an innovative approach to augment multispectral temporal datasets: the geospatial time machine (GTM). Our proposed methodology combines graph neural network (GNN) and generative adversarial network (GAN) architectures to generate comprehensive synthetic temporal data encompassing multivariate time series. The results demonstrate that imagery generated through backcasting can enhance the accuracy of downstream classification tasks by up to 53% in plant disease detection, particularly in the initial stages of analyzing a crop growth using multispectral and multitemporal datasets.

关键词： Data models Predictive models Accuracy image reconstruction Generative adversarial networks Graph neural networks Time series analysis Imputation remote sensing Crops Data augmentation deep learning generative adversarial networks (GANs) generative artificial intelligence (GenAI) graph neural networks (GNNs) multispectral

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4th International conference on Intelligent Systems and pattern recognition, ISPR 2024

4th International Conference on Intelligent Systems and Patt...

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4th International conference on Intelligent Systems and pattern recognition, ISPR 2024

ISBN: (纸本)9783031821523

The proceedings contain 77 papers. The special focus in this conference is on Intelligent Systems and pattern recognition. The topics include: Tunisian Sign Language recognition and Translation Using Deep Learning;Beyond Spatial: A Wavelet Fusion-Based Deep Learning CAD for Skin Cancer Diagnosis;integrating Machine Learning and Deep Learning Techniques for Enhanced Historical Script Classification;efficiency Meets Resilience: Accelerating Object Detection in Embedded Environments Through Compressive sensing;PMoET: Going Wider Than Deeper Using the Parallel Mixture of Experts Transformer for 3D Hand Gesture recognition;Enhancing Forest Fire Classification with Feature Selection and Machine Learning Based on PRISMA Hyperspectral Data;semantic Segmentation of Unmanned Aerial Vehicle remote sensing images Using SegFormer;handcrafted and Deep Features for Synthetic Data Generation in Offline Handwritten Signature Verification;refining U-Net Architecture Through Genetic Algorithms for Improved Skin Lesion image Segmentation;An RNN-LSTM Approach for Algerian Accent Identification;ensemble Methods for Predicting Cyanobacteria’s Potential Toxicity in Water Dams;cross-Organ Investigation of Tumor Histological Features Similarities Using Transfer Learning: A Case Study on Breast and Colorectal Tumors;behavior-Based Insider Threat Detection Using a Deep Neural Network;holistic Ontology Alignment Using Ontologies Embeddings;learning Different Separations in Branch and Cut: A Survey;Cancer Classification in Breast Imaging via Enhanced CNN Deep Learning Architecture;X-Ray Insights: A Siamese with CNN and Spatial Attention Network for Innovative Person Identification;hybrid Wiener Filter and Sharpening Filter for image De-blurring;feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection;artificial Intelligence-Based on Automatic Detection of Diabetic Eye Diseases : A Systematic Review;automated Breast Cancer Detection: A

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4th International conference on Intelligent Systems and pattern recognition, ISPR 2024

4th International Conference on Intelligent Systems and Patt...

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4th International conference on Intelligent Systems and pattern recognition, ISPR 2024

ISBN: (纸本)9783031821554

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4th International conference on Intelligent Systems and pattern recognition, ISPR 2024

4th International Conference on Intelligent Systems and Patt...

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4th International conference on Intelligent Systems and pattern recognition, ISPR 2024

ISBN: (纸本)9783031821493

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Proceedings of SPIE: image processing and pattern recognition in remote sensing II

Proceedings of SPIE: Image Processing and Pattern Recognitio...

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image processing and pattern recognition in remote sensing II

The proceedings contain 14 papers from the Proceedings of SPIE: image processing and pattern recognition in remote sensing II. The topics discussed include: exploitation of complementary sensors for precise ground-registration of sensed objects;segmentation of LANDSAT TM image using the Markov random field model toward a category classification of higher accuracy;real-time decision making on EO-1 using onboard science analysis;conflation technology in spatial data integration on the Internet;potential of IKONOS images for updating land-use-based map of China through data fusion and integration;and IEEE1394 and entropy reduction techniques as applied to remote sensing spacecraft communication systems.

关键词： remote sensing

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Signal processing

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Signal processing

作者： Geert Leus

出版社：ELSEVIER

Signal processingincorporates all aspects of the theory and practice of signal processing (analogue and digital). It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal *** areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal processing Systems; Software Developments; image processing; pattern recognition; Optical Signal processing; Digital Signal processing; Multi-dimensional Signal processing; Communication Signal processing; Biomedical Signal processing; Geophysical and Astrophysical Signal processing; Earth Resources Signal processing; Acoustic and Vibration Signal processing; Data processing; remote sensing; Signal processing Technology; Speech processing; Radar Signal processing; Sonar Signal processing; Industrial Applications;New Applications.

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Indoor Airflow Imaging Using Physics-Informed Schlieren Tomography

Indoor Airflow Imaging Using Physics-Informed Schlieren Tomo...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Arjun Teh Wael H. Ali Joshua Rapp Hassan Mansour Carnegie Mellon University Pittsburgh PA USA Mitsubishi Electric Research Laboratories Cambridge MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote temperature sensing of volumetric flows has a variety of applications, such as promoting thermal comfort, heat dissipation, or data center cooling. The emergence of background-oriented schlieren (BOS) imaging in recent years has enabled transparent flow visualization at minor costs. In this paper, we develop a framework for non-invasive volumetric indoor airflow estimation from a single viewpoint using BOS measurements and physics-informed reconstruction. Our framework utilizes a light projector that projects a pattern onto a target back wall and a camera that observes small distortions in the light pattern due to the change in the refractive index of the air as a result of the temperature variation. While the single-view BOS tomography problem is severely ill-posed, we regularize the reconstruction using a physics-informed neural network (PINN) that ensures that the reconstructed airflow is consistent with the coupled Boussinesq approximation of the incompressible Navier– Stokes and the heat transfer equations.

关键词： Temperature measurement Temperature sensors Temperature distribution Volume measurement Neural networks Refractive index Tomography Sensors Speech processing image reconstruction

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结合特征交互和融合的轻量级变化检测网络

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中国图象图形学报 2025年第3期30卷 870-882页

作者：王仁芳杨梓健邱虹王峰高广吴敦浙江万里学院大数据与软件工程学院宁波315100 上海海洋大学信息学院上海201306 宝略科技(浙江)有限公司宁波315042

目的利用深度学习开展变化检测是遥感智能解译热点研究方向之一。针对基于Transformer变化检测模型结构复杂、参数过多、训练耗时的问题,设计了一种融合特征交互和融合的轻量级变化检测网络(feature interaction and fusion lightweight... 详细信息

目的利用深度学习开展变化检测是遥感智能解译热点研究方向之一。针对基于Transformer变化检测模型结构复杂、参数过多、训练耗时的问题,设计了一种融合特征交互和融合的轻量级变化检测网络(feature interaction and fusion lightweight network,FIFLNet)。方法解码器中采用EfficientNet作为特征提取网络,其能利用模型的放缩(model scaling)能力来扩大模型的感受野。然后通过设计通道、像素交互模块(spatial and channel interact block)和浅层跳跃连接(low-level skip-connection)来实现浅层双时相的细节特征交互和上采样阶段的传递,以此增加模型对局部特征的判别精度。此外,利用特征融合分组卷积模块(feature fusion and groups convolution block,FFGCB)对双时相数据进行降维融合,降低了模型计算量。最后,设计了融合上采样模块(fusion upsampling block,FUB)对局部特征与全局特征进行融合还原,同时利用局部特征的细节、纹理来补偿全局特征细节的缺失。结果本文方法在两个遥感影像数据集LEVIR-CD(landearth view image retrieval building change detection dataset)和SYSU-CD(Sun Yat-sen University change detection dataset)上与13种SOTA(state-of-the-art)方法进行比较。实验结果表明,本文方法在变化检测任务中展现出显著优势。在定量评估方面,本文方法在LEVIR-CD和SYSU-CD数据集上的F1值分别达到91.51%和82.19%,较现有最优方法分别提升0.43%和1.58%。同时,模型计算效率优异,仅需1.66 GFLOPs的运算量和0.56 M的参数规模,显著低于所有对比方法。在定性分析方面,本文方法展现出更精确的检测区域定位、更低的漏检率,并能够保留更丰富的细节特征。结论本文提出的轻量级变化检测网络FIFLNet以较少的参数量和每秒浮点运算量获得了优越的性能,改善了小目标漏检、边界误检的情况,能够获得高质量的变化检测结果。

关键词：遥感影像变化检测局部特征特征交互轻量级网络

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