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检索条件"任意字段=Image Processing and Pattern Recognition in Remote Sensing"

共 7864 条记录，以下是691-700 订阅

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Abbreviating Labelling Cost for Sentinel-2 image Scene Classification Through Active Learning 10th

Abbreviating Labelling Cost for Sentinel-2 Image Scene Class...

引用

10th Iberian Conference on pattern recognition and image Analysis (IbPRIA)

作者： Raiyani, Kashyap Goncalves, Teresa Rato, Luis Univ Evora Sch Sci & Technol Dept Informat P-7000671 Evora Portugal

ISBN: (纸本)9783031048814;9783031048807

Over the years, due to the enrichment of paired-label datasets, supervised machine learning has become an important part of any problem-solving process. Active Learning gains importance when, given a large amount of freely available data, there's a lack of expert's manual labels. This paper proposes an active learning algorithm for selective choice of training samples in remote sensing image scene classification. Here, the classifier ranks the unlabeled pixels based on predefined heuristics and automatically selects those that are considered the most valuable for improvement;the expert then manually labels the selected pixels and the process is repeated. The system builds the optimal set of samples from a small and non-optimal training set, achieving a predefined classification accuracy. The experimental findings demonstrate that by adopting the proposed methodology, 0.02% of total training samples are required for Sentinel-2 image Scene Classification while still reaching the same level of accuracy reached by complete training data sets. The advantages of the proposed method is highlighted by a comparison with the state-of-the-art active learning method named entropy sampling.

关键词： Active learning Supervised classification Sentinel-2 image scene classification pattern recognition Training data reduction

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A Sinkhorn Regularized Adversarial Network for image Guided DEM Super-resolution Using Frequency Selective Hybrid Graph Transformer 27th

A Sinkhorn Regularized Adversarial Network for Image Guided...

引用

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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DMF-CL: Dense Multi-scale Feature Contrastive Learning for Semantic Segmentation of remote-sensing images 5th

DMF-CL: Dense Multi-scale Feature Contrastive Learning for S...

引用

5th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Song, Mengxing Li, Bicao Wei, Pingjun Shao, Zhuhong Wang, Jing Huang, Jie Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Capital Normal Univ Coll Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9783031189159;9783031189166

Recently, many segmentation methods based on supervised deep learning have been widely used in remote sensing images. However, these approaches often require a large number of labeled samples, which is difficult to obtain them for remote sensing images. Self-supervision is a new learning paradigm, and can solve the problem of lack of labeled samples. In this method, a large number of unlabeled samples are employed for pre-training, and then a few of labeled samples are leveraged for downstream tasks. Contrast learning is a typical self-supervised learning method. Inspired, we propose a Dense Multi-scale Feature Contrastive Learning Network (DMF-CLNet), which is divided into global and local feature extraction parts. Firstly, in the global part, instead of traditional ASPP, DenseASPP can obtain more context information of remote sensing images in a dense way without increasing parameters. Secondly, in the global and local parts, Coordinate Attention (CA) modules are introduced respectively to improve the overall performance of the segmentation model. Thirdly, in the global and local parts, the perceptual loss is calculated to extract deeper features. Two remote sensing image segmentation datasets are evaluated. The experimental results show that our model is superior to the current self-supervised contrastive learning methods and imageNet pre-training techniques.

关键词： Coordinate attention Contrast learning DenseASPP Perceptual loss

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A Two-Stage Road Segmentation Approach for remote sensing images 1

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26th International Conference on pattern recognition, ICPR 2022

作者： Li, Tianyu Comer, Mary Zerubia, Josiane Purdue University West LafayetteIN United States Inria Sophia Antipolis Valbonne France Université Côte d’Azur Nice France

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Many road segmentation methods based on CNNs have been proposed for remote sensing images in recent years. Although these techniques show great performance in various applications, there are still problems in road segmentation, due to the existence of complex backgrounds, illumination changes, and occlusions due to trees and cars. In this paper, we propose a two-stage strategy for road segmentation. A probability map is generated in the first stage by a selected network (ResUnet is used as a case study in this paper), then we attach the probability map image to the original RGB images and feed the resulting four images to a U-Net-like network in the second stage to get a refined result. Our experiments on the Massachusetts road dataset show the average IoU can increase up to 3% from stage one to stage two, which achieves state-of-the-art results on this dataset. Moreover, from the qualitative results, obvious improvements from stage one to stage two can be seen: fewer false positives and better connection of road lines. © 2023, Springer Nature Switzerland AG.

关键词： remote sensing

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031783401

The proceedings contain 958 papers. The special focus in this conference is on pattern recognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remote sensing images;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and

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Cultivated land recognition from remote sensing images based on improved deeplabv3 model

Cultivated land recognition from remote sensing images based...

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2022 Chinese Automation Congress, CAC 2022

作者： Yan, Yangtian Gao, Yan Shao, Liwei Yu, Linquan Zeng, Wentao Beijing Institute of Technology College of Automation Beijing China Beijing Institute of Technology Zhongshan Research Institute Zhongshan China

ISBN: (纸本)9781665465335

With the development of remote sensing image technology and semantic segmentation technology, using remote sensing image to segment cultivated land area has become an important and challenging task, The current semantic segmentation algorithm used in remote sensing image cultivated land segmentation has some problems, such as low detection accuracy, unable to distinguish narrow roads and so on. This paper is based on DeepLab v3 algorithm. In order to improve its ability to distinguish narrow targets, we improve the network structure, use ResNeSt network, and introduce feature pyramid structure;By using the CCNet self-attention module, the network's ability to obtain image context information is improved, so as to improve the segmentation accuracy;Adding post-processing module in reasoning process. Reduce the false detection rate of the model. The experimental results show that compared with the original DeepLab v3 and other commonly used segmentation models, the improved DeepLab v3-RFCT model improves the detection accuracy and enhances the ability to distinguish narrow roads. © 2022 IEEE.

关键词： Semantic Segmentation

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031781919