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

共 4028 条记录，以下是181-190 订阅

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An Improved Bilateral Filtering Based on Curvature Feature for Point Cloud Denoising in Road Surface Detection 12

An Improved Bilateral Filtering Based on Curvature Feature f...

引用

SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Chen, Si Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Technology Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

To effectively remove noise from the point clouds obtained for road surface defect detection while preserving the feature information, a bilateral filtering algorithm based on curvature features is proposed for denoising road surface defect point clouds. First, divide the noise points in the point cloud into several regions based on their distribution characteristics and curvature features. Then use statistical filtering to remove noise points in the obvious noise area and the flat area. Finally, an improved bilateral filtering algorithm is applied to smooth the noise points in the complex detail area. © 2024 SPIE.

关键词： Surface defects

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Multi-Sensor Information Fusion Algorithm Based on Edge Information

Multi-Sensor Information Fusion Algorithm Based on Edge Info...

引用

2023 International conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2023

作者： Liu, Chunbao Jilin University School of Preparation Education Changchun Jilin130012 China

ISBN: (纸本)9798350301397

Multi-sensor information fusion is a new type of information fusion technology developed in recent years. In machine vision, edge information is a very important type of information, and edge extraction is the foundation and prerequisite for subsequent processing. This method also has certain applications in medical diagnosis, human simulation, and control of certain complex industrial processes. The idea of multi-sensor data fusion may seem simple and straightforward, but it is a very difficult task in practical applications. image registration is a branch of image pattern recognition and multi image fusion, which has great application value in remote sensing, military and other aspects. On this basis, a new multi-sensor information fusion method is proposed. Firstly, the basic principles of information fusion technology were elaborated and corresponding processing modes were provided. On this basis, this article explores the current development status and existing problems of information fusion from three aspects: the architecture of information fusion, the application scope of information fusion, and the algorithms of information fusion. On this basis, the development direction of information fusion technology is proposed. © 2023 IEEE.

关键词： remote sensing

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Embedding Spatial Relations in Visual Question Answering for remote sensing 26

Embedding Spatial Relations in Visual Question Answering for...

引用

26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Faure, Maxime Lobry, Sylvain Kurtz, Camille Wendling, Laurent Univ Paris Cite LIPADE F-75006 Paris France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

remote sensing images carry a wealth of information that is not easily accessible to end-users as it requires strong technical skills and knowledge. Visual Question Answering (VQA), a task that aims at answering an open-ended question in natural language from an image, can provide an easier access to this information. Considering the geographical information contained in remote sensing images, questions often embed an important spatial aspect, for instance regarding the relative position of two objects. Our objective is to better model the spatial relations in the construction of a ground-truth database of image/question/answer triplets and to assess the capacity a VQA model has to answer these questions. In this article, we propose to use histograms of forces to model the directional spatial relations between geo-localized objects. This allows a finer modeling of ambiguous relationships between objects and to provide different levels of assessment of a relation (e.g. object A is slightly/strictly to the west of object B). Using this new dataset, we evaluate the performances of a classical VQA model and propose a curriculum learning strategy to better take into account the varying difficulty of questions embedding spatial relations. With this approach, we show an improvement in the performances of our model, highlighting the interest of embedding spatial relations in VQA for remote sensing applications.

关键词： Training Visualization Histograms Feature extraction Question answering (information retrieval) Spatial databases pattern recognition

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processing landslide hazard prediction using satellite image segmentation 3

Processing landslide hazard prediction using satellite image...

引用

3rd International conference on Geographic Information and remote sensing Technology, GIRST 2024

作者： Chiu, Chenyuan Hsu, Sung-Chi Huang, Yishuo Zhang, Nan Peng, Chen Labib Ul Islam, Syed School of Civil Engineering Jiaying University Guangdong Meizhou514015 China Dept. of Construction Engineering Chaoyang Univ. of Technology 168 Jifong East Rd. Wufong Taichung41349 Taiwan School of Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Dept. of Civil Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9781510689077

remote sensing technology has become one of the indispensable techniques for the investigation of geologic disasters at home and abroad. However, the interpretation of bare ground classification by satellite multispectral images is often interfered by atmospheric clouds in mountainous areas, shadows on the mountains, and the phenomenon of Speckle Signals, which seriously affects the correctness of the interpretation images, and is prone to errors such as misjudgment or omission. In this study, the SPOT 5 satellite images of the study area were segmented by applying the Multiphase Level Set Framework using MATLAB computation. The results show that the problem of the speckle signals can be eliminated efficiently and quickly, and the complex terrain and features can be segmented into simple objects, which can solve the problem of the spatial recognition rate without being affected by the resolution of the image itself, and improve the image accuracy. This can solve the problem of spatial recognition without being affected by the resolution of the image itself, and improve the accuracy of satellite image interpretation, which can be used as a possible landslide trend. © 2025 SPIE.

关键词： Landslides

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Identification method of regional soil erosion characteristics based on drone low altitude remote sensing technology 5

Identification method of regional soil erosion characteristi...

引用

5th International conference on Geology, Mapping, and remote sensing, ICGMRS 2024

作者： Xie, Xiang Duan, Wenming Yin, Zhikui Duan, Wenguang Bu, Junru Lu, Yucun Qingdao Water Conservancy Survey And Design Institute CO. LTD. Jinan China Shandong Lvjing Ecological Engineering Design Co. Ltd Jinan China Zouping Railway Service Center Zouping China

ISBN: (纸本)9781510681712

In order to accurately identify the characteristics of regional soil erosion, this paper proposes a method for identifying the characteristics of regional soil erosion based on unmanned aerial vehicle low altitude remote sensing technology. By installing high-resolution sensors on drones, multispectral remote sensing images of the target area are obtained, and advanced image processing and analysis methods are used to extract features related to soil erosion. The experimental results show that the regional soil erosion feature recognition method based on drone low altitude remote sensing technology can accurately identify the types of soil erosion, providing strong support for soil and water conservation and ecological environment protection. The conclusion indicates that this technology has the advantages of efficiency, accuracy, and flexibility, and can be widely applied in regional soil erosion monitoring and assessment, providing scientific basis for sustainable development. © 2024 SPIE.

关键词： Drones

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remote sensing image Scene Classification via Multi-Level Representation Learning 26

Remote Sensing Image Scene Classification via Multi-Level Re...

引用

26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Fu, Wei Yang, Lishuang Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

remote sensing image scene classification (RSSC), which assigns semantic labels to remote sensing images, is very important for remote sensing image interpretation. Thanks to the rapid development of deep learning, RSSC achieves significant breakthroughs by the use of convolutional neural network (CNN). However, CNN relies on local receptive fields and is difficult to capture long-range and global scene information. Moreover, the information of salient objects, which contributes to discriminate the category of scenes (e.g., airplanes indicate the airport scene), should be also exploited. To address this issue, a deep learning method, named multi-level representation learning (MLRL), is proposed to collaboratively extract pixel-level, patch-level, and object-level features, which respectively contain local, global, and object-oriented information. Specifically, pixel-level features are obtained by pixel-wise convolution operations within a CNN. Patch-level features are achieved by a patch-wise self-attention network. Object-level features are acquired by applying a CNN to a cropped sub-image, which conveys important information of salient objects. To this end, a three-branch network structure to respectively extract above features, is built. Finally, a decision fusion method is adopted to integrate multi-level features, and gives rise to refined classification results. Experiments conducted on widely-used datasets demonstrate the effectiveness of the proposed method.

关键词： Deep learning Representation learning Measurement image analysis Fuses Semantics Feature extraction

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image Augmentations in Planetary Science: Implications in Self-Supervised Learning and Weakly-Supervised Segmentation on Mars 26

Image Augmentations in Planetary Science: Implications in Se...

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26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Kossmann, Dominik Matei, Arthur Wilhelm, Thorsten Fink, Gernot A. TU Dortmund Univ Dept Comp Sci Dortmund Germany TU Dortmund Univ Dept Elect Engn & Informat Technol Dortmund Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Research on the use of augmentations in physically constrained remote sensing scenarios, like the analysis of Martian surface data, is largely unexplored. In this work we present an analysis on how reasonable augmentation strategies can be selected which are class agnostic and respect physical plausibility in supervised and weakly-supervised tasks. Additionally, we present the first results of self-supervised learning on Martian surface data, discuss the importance of physically plausible augmentations in the context of self-supervised learning, specifically contrastive learning, and provide a comprehensive overview of the generalization properties induced by different augmentation strategies with the help of geomorphic maps.

关键词： Mars image segmentation Self-supervised learning pattern recognition Task analysis remote sensing

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Accurate Identification Technology for Cross Regional Behavior of Power Operators in Power Grid Dispatch Operations

Accurate Identification Technology for Cross Regional Behavi...

引用

2024 International conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Li, Wei Hua, Jie Xu, Chuan Wang, Zhenyu Hu, Chao Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Foshan528000 China NARI Information & Communication Technology Co. Ltd. Jiangsu Nanjing210003 China

ISBN: (纸本)9781510680012

In order to accurately identify the crossing behavior of power operators in the power grid dispatch operation area and maintain the safety of power grid operation, a technology for accurately identifying the crossing behavior of power operators in the power grid dispatch operation area is proposed. Collect monitoring video images of the power grid dispatch operation area, and segment the monitoring video images into target foreground and background based on information entropy;Extract time-domain and frequency-domain characteristics of cross behavior of power operators;recognition of cross regional behavior in power grid dispatch operations based on local gradient feature histograms. The experimental results demonstrate that this method can improve recognition accuracy, recall, and efficiency, and contribute to maintaining the safe operation of the power grid. © 2024 SPIE.

关键词： image segmentation

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Revisiting Near/remote sensing with Geospatial Attention

Revisiting Near/Remote Sensing with Geospatial Attention

引用

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

作者： Workman, Scott Rafique, M. Usman Blanton, Hunter Jacobs, Nathan DZYNE Technol Fairfax VA 22031 USA Kitware Inc Clifton Pk NY USA Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods.

关键词： image segmentation Computer vision Social networking (online) Computational modeling Computer architecture Geospatial analysis Sensors

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Research on natural disaster target change detection method based on deep learning 3

Research on natural disaster target change detection method ...

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3rd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Yin, Yutong Hohai University School of Computer and Information Nanjing211100 China

ISBN: (纸本)9781665472784

In recent years, with the increasing global climate problems, our country has been relying on remote sensing technology for early warning and post-disaster assessment in the field of disaster prevention and control for some time. However, there are many kinds of natural disasters, especially sudden natural disasters, such as earthquakes, floods, typhoons and mudslides, which are often too late to prevent, and there is also a strong recurrence between disasters, which is very likely to cause massive data interference to remote sensing recognition. Compared with the traditional convolutional neural network-based remote sensing image recognition processing, this project proposes to use the Faster-RCNN target detection algorithm, which adds the Region Proposal Networks (RPN) module to the traditional Fast CNN to generate the algorithm, making the target change detection speed and accuracy greatly improved. The change detection algorithm is based on the traditional Fast CNN. This change detection model has the natural advantage of processing remote sensing data, which can automatically and multi-levelly extract the features of complex objects, learn to discover the geometric and spatial relationships of feature targets in images from massive data, and solve complex classification or regression problems through multi-layer nonlinear networks, thus significantly improving the accuracy of remote sensing image interpretation. After the model design is completed, in order to improve the detection accuracy of the model, it is proposed to train the Faster-RCNN change detection model by acquiring a large number of original samples of remote sensing images with manually expanded samples. © 2023 IEEE.

关键词： remote sensing

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