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27th International conference on pattern recognition, ICPR 2024

作者： Paul, Subhajit Gupta, Ashutosh ISRO Ahmedabad India

ISBN: (纸本)9783031784972

Digital Elevation Model (DEM) is an essential aspect in the remote sensing (RS) domain to analyze various applications related to surface elevations. Here, we address the generation of high-resolution (HR) DEMs using HR multi-spectral (MX) satellite imagery as a guide by introducing a novel hybrid transformer model consisting of Densely connected Multi-Residual Block (DMRB) and multi-headed Frequency Selective Graph Attention (M-FSGA). To promptly regulate this process, we utilize the notion of discriminator spatial maps as the conditional attention to the MX guide. Further, we present a novel adversarial objective related to optimizing Sinkhorn distance with classical GAN. In this regard, we provide both theoretical and empirical substantiation of better performance in terms of vanishing gradient issues and numerical convergence. Based on our experiments on 4 different DEM datasets, we demonstrate both qualitative and quantitative comparisons with available baseline methods and show that the performance of our proposed model is superior to others with sharper details and minimal errors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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pattern recognition AND DIGITAL image processing AS APPLIED TO remote sensing IN INDIA.

PATTERN RECOGNITION AND DIGITAL IMAGE PROCESSING AS APPLIED ...

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Proceedings - IEEE Computer Society conference on pattern recognition and image processing.

作者： Deekshatulu, B.L.

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Multi Class Spectral Discriminative Convolutional Neural Network Towards Hyperspectral image Classification of Crop Disease on Inclusion of Spectral Spatial Feature Fusion Mechanism 4

Multi Class Spectral Discriminative Convolutional Neural Net...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Boopathi Raja, A.S. Poovarasan, K. Udhayakiran, S. Muhammed Aleem, Shaik Tharageswari, K. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Crop diseases occurs due to bacterial, viral, parasitic and fungal infection leads to various challenges to farmer and agricultural based business as crop diseases effects the normal growth of the crop and it spreads to other crops surrounding it. Especially those crop infection leads to great economic damage to the farmers and to the entire country. In order to prevent, classify and mitigate those crop infections, effective measures have to be carried out. Manual observation by biologist and agriculturist leads to misinterpretation and time consuming. In particular, remote sensing technologies using hyperspectral imaging techniques helps to monitor and acquire the agriculture information in various target regions effectively. Due to development of computer vision technique and artificial intelligence approaches in form of machine learning and deep learning architectures, countermeasure against the crop disease can be achieved easily on processing the acquired information in form of hyperspectral images. However traditional architecture faces multiple challenges in form of the spectral signatures unmixing as boundaries of the objects of different diseases types is very complex to be filtered. Thus, a new spectral unmixing based object recognition method has to be constructed to categorizes different form of crop disease in the agriculture region. In this paper, Multi Class Spectral Discriminative Convolution Neural Network(MCSD-CNNN) using spectral and spatial feature fusion towards hyperspectral image classification for detect, classify and mitigate the crop diseases on various climate and various regions. Initially image preprocessing is carried out to eliminate the spectral noises. Preprocessed image is projected to convolution layer of the network to extract the endmembers of the spectral band representing the various crop species in the different climate and spatial regions. Extracted end member of the spectral band is represented spectral disease index map. Th

关键词： Convolutional neural networks

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Automatic Weight Allocation: optimizing remote sensing image retrieval from contrastive learning perspective

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Multimedia Systems 2025年第3期31卷 1-19页

作者： Wang, Sijia Ge, Yun Liu, Qiyang Zeng, Yan School of Software Nanchang Hangkong University Nanchang China Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Nanchang China

Traditional supervised learning methods achieve remarkable performance in high-resolution remote sensing image retrieval, but are limited by the dependence on large-scale annotated images. Contrastive learning can leverage unlabeled images to learn powerful visual features, demonstrating its potential in many unsupervised tasks. Moreover, hash algorithms show significant potential in the field of image retrieval with their advantages in efficiency and storage. Therefore, we propose the Contrastive Hashing Framework based on Automatic Weight Allocation. The framework employs a two-stage training strategy. In the feature learning stage, we propose the Automatic Weighted Contrastive Loss (AWCLoss). It incorporates Gaussian weighting and dynamic adjustment strategies to improve loss functions, enabling them to focus on the distinctiveness and importance of samples. Gaussian weighting assigns different weight values based on the similarity of sample pairs, enhancing the learning of critical sample pairs. Meanwhile, the dynamic adjustment strategy sets a threshold to identify hard negative samples and then adjusts the weight values to weaken the model from being disturbed by hard negative samples. In the hashing learning stage, a hashing layer is added to the end of the network, which converts high-dimensional representations into hash codes. The Quantization loss is introduced to learn the hash codes so that the semantic similarity structure between data can be preserved in hamming space. Additionally, the AWCLoss is utilized to enhance the discriminative power of the hash codes. Extensive experiments on three remotely sensed datasets UCM, AID and NWPU-RESISC45 have demonstrated the significant superiority of our approach in remote sensing image retrieval. Our source code is available at https://***/WANGSJ77/AWCH .

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Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review

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remote sensing 2025年第4期17卷 698-698页

作者： Wang, Shaohua Xu, Dachuan Liang, Haojian Bai, Yongqing Li, Xiao Zhou, Junyuan Su, Cheng Wei, Wenyu Hainan Aerosp Informat Res Inst Key Lab Earth Observat Hainan Prov Sanya 572029 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing & Digital Earth Beijing 100101 Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Lanzhou Jiaotong Univ Sch Architecture & Urban Planning Lanzhou 730070 Peoples R China

Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques-including image classification, object detection, semantic segmentation, and change detection-to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture.

关键词： deep learning disease detection plant diseases and pests image classification object detection convolutional neural network

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pattern recognition in Apple Orchards During Dormancy and Bud Development 4th

Pattern Recognition in Apple Orchards During Dormancy and ...

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4th International conference on Smart Multimedia, ICSM 2024

作者： Sehgal, Bhavya Gandhi, Ritik Cheng, Irene Department of Computing Science University of Alberta EdmontonABT6G 2R3 Canada

ISBN: (纸本)9783031824746

Effective management of apple orchards during dormancy and bud development stages is crucial for optimizing fruit production and tree health. Automation using computer vision and deep learning techniques offers a promising solution for tree segmentation and labeling in complex outdoor environments. However, existing methods face challenges in accurately segmenting trees with bare branches and adapting to changing perspectives. This paper presents innovative solutions for bare tree segmentation and labeling, combining depth sensing and advanced image processing with deep learning approaches. The proposed methods address the limitations of current techniques, offering improved accuracy and robustness. We also introduce a novel tree labeling algorithm that utilizes spatial information and YOLOv4 for accurate tree identification and labeling across video frames. Experimental results demonstrate the effectiveness of our approach in handling complex outdoor conditions and bare tree segmentation. Our research contributes to advancing automated orchard management and precise monitoring during dormancy and bud development stages, enhancing fruit quality and overall orchard productivity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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

Proceedings of SPIE: Image processing and pattern recognitio...

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image processing and pattern recognition in remote sensing

The proceedings contains 40 papers from the conference on Proceedings of SPIE: image processing and pattern recognition in remote sensing. The topics discussed include: image fusion of Hyperion and Ikonos imagery;managing and distributing remote sensing images based on metadata;new intensity interpolation for resampling of remote sensing imagery;and line extraction in multispectral images.

关键词： remote sensing

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Semantic Segmentation of Unmanned Aerial Vehicle remote sensing images Using SegFormer 4th

Semantic Segmentation of Unmanned Aerial Vehicle Remote Sen...

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

作者： Spasev, Vlatko Dimitrovski, Ivica Chorbev, Ivan Kitanovski, Ivan Faculty of Computer Science and Engineering University Ss Cyril and Methodius Skopje Macedonia

ISBN: (纸本)9783031821554

The escalating use of Unmanned Aerial Vehicles (UAVs) as remote sensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remote sensing images face limitations in resolution and weather susceptibility, UAV remote sensing, employing low-speed unmanned aircraft, offers enhanced object resolution and agility. The advent of advanced machine learning techniques has propelled significant strides in image analysis, particularly in semantic segmentation for UAV remote sensing images. This paper evaluates the effectiveness and efficiency of SegFormer, a semantic segmentation framework, for the semantic segmentation of UAV images. SegFormer variants, ranging from real-time (B0) to high-performance (B5) models, are assessed using the UAVid dataset tailored for semantic segmentation tasks. The research details the architecture and training procedures specific to SegFormer in the context of UAV semantic segmentation. Experimental results showcase the model’s performance on benchmark dataset, highlighting its ability to accurately delineate objects and land cover features in diverse UAV scenarios, leading to both high efficiency and performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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1st International conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

1st International Conference on Pattern Analysis and Machine...

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1st International conference on pattern Analysis and Machine Intelligence, ICPAMI 2024

ISBN: (纸本)9789819633487

The proceedings contain 28 papers. The special focus in this conference is on pattern Analysis and Machine Intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design of Machine Vision-Based Waste Sorting System;ECLNet: Efficient Convolution with Lite Transformer for 3D Medical image Segmentation;exploring High-Performance 3D Object Detection with Partial Depth Completion;full-Scale Network for remote sensing Object Detection;Detection of Pedestrian Movement Poses in High-Speed Autonomous Driving Environments Using DVS;city-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model;an Efficient Transformer-Based Network for remote sensing image Change Detection;the Method for Three-Dimensional Visual Measurement of Circular Markers Based on Active Fusion Technology;intelligent image recognition and Classification Technology in Digital Media;Indoor Visible Light Positioning System Based on the image Sensor and CNN-GRU Fusion Neural Network;Stock Investor Sentiment Analysis Based on NLP;Novel Audiobook System Based on BERT;student Enrollment Consultation Q&A Robot Based on Large Language Model;family Doctor Model Training Based on Large Language Model Tuning;composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection;Improved CNN-GRU RF Fingerprint Feature recognition Method Based on Comb Filter;emotional State recognition of English Learners Based on Deep Learning;Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis;visual recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning;quadruped Robot System Based on Proprioceptive Vision and Complex Ground Mobility Capabilities;a Simulated Dataset to Evaluate the Visual-Inertial Odometry Algorithms.

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd International conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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