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

共 4027 条记录，以下是301-310 订阅

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Deep unfolding for hyper sharpening using a high-frequency injection module

Deep unfolding for hyper sharpening using a high-frequency i...

引用

2023 IEEE/CVF conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Mifdal, Jamila Tomás-Cruz, Marc Sebastianelli, Alessandro Coll, Bartomeu Duran, Joan European Space Agency P-lab ESRIN Frascati00044 Italy Universitat de les Illes Balears DMI & IAC3 Cra. de Valldemossa km. 7.5 PalmaE-07122 Spain

ISBN: (纸本)9798350302493

The fusion of multi-source data with different spatial and spectral resolutions is a crucial task in many remote sensing and computer vision applications. Model-based fusion methods are more interpretable and. flexible than pure data-driven networks, but their performance depends greatly on the established fusion model and. the hand-crafted, prior. In this work, we propose an end-to-end trainable model-based. network for hyperspectral and panchromatic image fusion. We introduce an energy functional that takes into account classical observation models and. incorporates a high-frequency injection constraint. The resulting optimization function is solved by a forward-backward splitting algorithm and. unfolded into a deep-learning framework that uses two modules trained, in parallel to ensure both data observation fitting and constraint compliance. Extensive experiments are conducted, on the remote-sensing hyperspectral PRISMA dataset and on the CAVE dataset, proving the superiority of the proposed deep unfolding network qualitatively and quantitatively. © 2023 IEEE.

关键词： remote sensing

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remote sensing image Registration Via Feature Fusion and Enhanced Matching 6

Remote Sensing Image Registration Via Feature Fusion and Enh...

引用

6th International conference on Electronic Information Technology and Computer Engineering, EITCE 2022

作者： Li, Xianjing Chen, Ying Zhang, Wencheng Wang, Jiahao Wang, Wei School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9781450397148

image registration is a basic problem in image analysis and image processing. image registration has important applications in aerial image fusion, pattern recognition, three-dimensional reconstruction and other fields. Aiming at the problem of low registration accuracy and mismatching in remote sensing image registration, this paper proposes to use the Involution kernel to improve the ResNext network in the feature extraction stage and combines the SPANet attention mechanism with an improved ResNext network to improve the feature extraction ability of the network. In the feature matching stage, an enhanced matching method is proposed, which uses cross-correlation and nearest neighbor to second nearest neighbor ratio to filter out mismatched points to cope with complex images and background interference. The experimental results show that the proposed algorithm can achieve superior results in a variety of indexes compared with other algorithms, which proves that the proposed algorithm is effective. © 2022 Association for Computing Machinery.

关键词： remote sensing

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Target Detection Algorithm for Drone Aerial images based on Deep Learning

Target Detection Algorithm for Drone Aerial Images based on ...

引用

2024 International conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Liu, Tao Zhang, Bohan Heilongjiang Institute of Technology College of Computer Science and Technology Harbin China China University of Geosciences School of Information Engineering Wuhan China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China

ISBN: (纸本)9798350375442

In recent years, the rapid development of drones has brought tremendous changes to many fields. From refined management in agriculture and forestry to aerial surveying in urban planning, the popularity of UAVs provides efficient and accurate data support for various industries, promoting social intelligence and technological progress. Meanwhile, deep learning methods represented by computer vision provide powerful tools for research such as image processing and object recognition. The research results of HE-YOLOX-ASFF (Highly Efficient You Only Look Once eXtend with Adaptive Spatial Feature Fusion) algorithm for target detection in UAV aerial images in this article can help improve the recognition accuracy and efficiency of drone remote sensing images, and apply it to fields such as smart agriculture, urban management, and environmental monitoring. At present, detection technologies for large-scale targets have achieved good detection results in clear backgrounds. However, drone aerial images often have complex backgrounds, small target volumes, limited available features, and high target density, making them prone to occlusion. Due to the above reasons, existing target detection algorithms have low detection accuracy for weak and small targets in aerial images under complex backgrounds, and are prone to missed and false detections. Therefore, the detection method of weak aerial targets in complex backgrounds remains a challenging issue. The experimental results show that in identifying pedestrians and bicycles, the parameter values are relatively high, at 33.3 and 65.1, respectively, while the FPS (frames per second) is relatively low, at 63 and 38, respectively. This indicates that the algorithm requires more parameters to process these targets and may affect processing speed. © 2024 IEEE.

关键词： Drones

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OmniCity: Omnipotent City Understanding with Multi-level and Multi-view images

OmniCity: Omnipotent City Understanding with Multi-level and...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Li, Weijia Lai, Yawen Xu, Linning Xiangli, Yuanbo Yu, Jinhua He, Conghui Xia, Gui-Song Lin, Dahua Sun Yat Sen Univ Guangzhou Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9798350301298

This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, OmniCity contains multi-view satellite images as well as street-level panorama and monoview images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geolocations in New York City. To alleviate the substantial pixel-wise annotation efforts, we propose an efficient street-view image annotation pipeline that leverages the existing label maps of satellite view and the transformation relations between different views (satellite, panorama, and mono-view). With the new OmniCity dataset, we provide benchmarks for a variety of tasks including building footprint extraction, height estimation, and building plane/instance/fine-grained segmentation. Compared with existing multi-level and multi-view benchmarks, OmniCity contains a larger number of images with richer annotation types and more views, provides more benchmark results of state-of-the-art models, and introduces a new task for fine-grained building instance segmentation on street-level panorama images. Moreover, OmniCity provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation. The OmniCity dataset as well as the benchmarks will be released at https://***/omnicity/.

关键词： Photogrammetry and remote sensing

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Improved remote sensing image Target Detection Algorithm Based on YOLOv5

Improved Remote Sensing Image Target Detection Algorithm Bas...

引用

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

作者： Xiaodi Zhang Yan Xu Guorong Liu Ping Hu Zhaoyuan Chen Fang Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

remote sensing information processing plays an important role in many fields such as environmental protection, urban planning, military reconnaissance and so on. However, there are problems in remote sensing images such as complex background, large size change, small target, etc. A remote sensing image target detection algorithm to improve YOLOv5 is proposed. Using a certain percentage of data enhancement technology at the input side to enrich the data features and improve the model generalization ability and robustness; for the problem of small targets in remote sensing images, SPD-Conv module is added to reduce the loss of fine-grained information and improve the detection performance of small targets; the design uses the DCNv2 module to replace part of the neck structure of the C3 layer, which breaks through the limitation of traditional convolution and improves the feature extraction ability of the network in remote sensing images. Experimental results show that the improved YOLOv5 model improves the detection accuracy by 3.9%.

关键词： YOLO Training Adaptation models Data enhancement Urban planning Feature extraction Data models Robustness Sensors remote sensing

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Proceedings - 2023 5th International conference on pattern recognition and Intelligent Systems, PRIS 2023

Proceedings - 2023 5th International Conference on Pattern R...

引用

5th International conference on pattern recognition and Intelligent Systems, PRIS 2023

ISBN: (纸本)9781450399968

The proceedings contain 19 papers. The topics discussed include: building segmentation from remote sensing image via dwt attention networks;reconstructing 3D shapes as an union of boxes from multi-view images;three-dimensional sphere recognition and tracking based on YOLO;LLFormer: an efficient and real-time LiDAR lane detection method based on transformer;frequency-split inception transformer for image super-resolution;MSYOLOF: multi-input-single-output encoder network with tripartite feature enhancement for object detection;exploration of transfer learning capability of multilingual models for text classification;policy updating methods of Q learning for two player bargaining game;multi-population Runge Kutta optimizer based on Gaussian disturbance;survey of the formal verification of operating systems in power monitoring system;and a study on the line loss index of a substation area based on cooperative games with multiple influencing factors.

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A Study on Deep Learning-Based image Target Identification Techniques 5th

A Study on Deep Learning-Based Image Target Identification T...

引用

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

作者： Wang, Yining College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China

ISBN: (纸本)9789819751808

This study aims to explore deep learning-based image target recognition methods to improve the performance of target detection and classification in the field of computer vision. The experiments use satellite-acquired remote sensing images of moving objects, including four target categories such as buses, cars, bicycles and ships. In model training, we performed parameter optimisation by stochastic gradient descent algorithm and set the training period to 12. To validate the model’s effectiveness, we compare the improved R-CNN (Region-based Convolutional Neural Network), traditional CNN (Convolutional Neural Network) and Faster-RCNN (Faster Region-based Convolutional Neural Network) models, and generate confusion matrices to evaluate their performance. The experimental results show that the improved model exhibits excellent performance in recognition of different types of target objects, especially for larger targets. This research provides reliable experimental support for the application of deep learning in the field of image target recognition and is expected to promote further development and innovation in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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DSDN-Net: An Effective Network for Semantic Segmentation in Open-Pit Coal Mining Areas for Land Cover recognition

DSDN-Net: An Effective Network for Semantic Segmentation in ...

引用

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

作者： Jiaqi Li Ming Ma College of Computer Science-College of Software Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, existing methods in land cover recognition in open-pit coal mining areas face the issue of insufficient accuracy due to multiscale and blurred boundaries when processing remote sensing images. This paper introduces a remote sensing image semantic segmentation network, DSDN-Net, to tackle the issues. DSDN-Net adopts MobileNetV2 as the backbone, and a spatial pyramid pooling structure, Dynamic Snake Dense-ASPP (DSDN-ASPP), which enhances the model’s receptive field based on cross-layer connections is designed to increase the model’s receptive field by introducing dynamic snake convolution and utilizing depthwise convolutions with different kernel sizes, allowing the model to focus more on spatial features in remote sensing images. To handle blurred boundaries in remote sensing image, the decoder of DSDN-Net incorporates a Convolutional Block Attention Module (CBAM) to enhance precision. A dataset containing 5440 remote sensing images from the open-pit coal mining area is constructed using high-resolution remote sensing image. Experimental results on the open-pit coal mining area dataset demonstrate that the proposed DSDN-Net outperforms existing methods in multiple performance metrics.

关键词： image recognition Accuracy Convolution Target recognition Semantic segmentation Land surface Signal processing algorithms Feature extraction Coal mining remote sensing

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Masked Vision Transformers for Hyperspectral image Classification

Masked Vision Transformers for Hyperspectral Image Classific...

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2023 IEEE/CVF conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Scheibenreif, Linus Mommert, Michael Borth, Damian University of St. Gallen Aiml Lab School of Computer Science Switzerland

ISBN: (纸本)9798350302493

Transformer architectures have become state-of-the-art models in computer vision and natural language processing. To a significant degree, their success can be attributed to self-supervised pre-training on large scale unlabeled datasets. This work investigates the use of self-supervised masked image reconstruction to advance transformer models for hyperspectral remote sensing imagery. To facilitate self-supervised pre-training, we build a large dataset of unlabeled hyperspectral observations from the EnMAP satellite and systematically investigate modifications of the vision transformer architecture to optimally leverage the characteristics of hyperspectral data. We find significant improvements in accuracy on different land cover classification tasks over both standard vision and sequence transformers using (i) blockwise patch embeddings, (ii) spatialspectral self-attention, (iii) spectral positional embeddings and (iv) masked self-supervised pre-training1. The resulting model outperforms standard transformer architectures by +5% accuracy on a labeled subset of our EnMAP data and by +15% on Houston2018 hyperspectral dataset, making it competitive with a strong 3D convolutional neural network baseline. In an ablation study on label-efficiency based on the Houston2018 dataset, self-supervised pre-training significantly improves transformer accuracy when little labeled training data is available. The self-supervised model outperforms randomly initialized transformers and the 3D convolutional neural network by +7-8% when only 0.1-10% of the training labels are available. © 2023 IEEE.

关键词： Embeddings

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remote sensing Based Advance image Captioning Improved Feature Attention 7

Remote Sensing Based Advance Image Captioning Improved Featu...

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7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Sattar, Ammara Assam, Muhammad Alahmadi, Tahani Jaser Bhatti, Uzair Aslam Tang, Hao Aamir, Muhammad Harbin Engineering University Harbin China University of Science and Technology Department of Software Engineering KPK Bannu Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia School of Information and Communication Engineering Hainan University Haikou China College of Computer Science Huanggang Normal University Huanggang China

ISBN: (纸本)9798350350890

In the domain of image description generation, the focal point mechanism is crucial for highlighting the most relevant features within an image, thereby influencing the quality of generated descriptions. To address this challenge, we introduce an innovative optimization module called the Enhanced Focal (EF) mechanism, which builds upon the traditional multi-head focal point mechanism. The EF module is designed to assess the correlation levels between objects within an image, thereby guiding the process of subtitle generation more effectively. The EF mechanism integrates the output of the multi-head focal point mechanism with the current context (i.e., the query) to generate two components: an 'information vector' and an 'attention gate.' The 'attention gate' is then applied to the 'information vector' to create a refined focal point mechanism. This refined mechanism undergoes element-wise multiplication with the 'information vector,' producing attention information that reveals a strong correlation between the target and the background in the image. This process leverages the Long Short-Term Memory (LSTM) mechanism to enhance the model's capability to capture intricate relationships. Upon training and evaluating our enhanced model on the MS COCO dataset, we observed significant improvements in key evaluation metrics. The BLEU-1 and METEOR scores reached 73.01% and 28.81%, respectively, showcasing the effectiveness of the proposed EF mechanism in improving image description generation. © 2024 IEEE.

关键词： Data description

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