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

共 707 条记录，以下是61-70 订阅

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Multi-Sensor Information Fusion Algorithm Based on Edge Information

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

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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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Aircraft Type recognition in remote sensing images using Mean Interval Kernel

Aircraft Type Recognition in Remote Sensing Images using Mea...

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International conference on image processing and Vision Engineering (IMPROVE)

作者： Sharma, Jaya Datla, Rajeshreddy Sravani, Yenduri Chalavadi, Vishnu Mohan, Krishna C. Indian Inst Technol Hyderabad Dept Comp Sci & Engn Hyderabad India Adv Data Proc Res Inst ADRIN Dept Space Secunderabad India

ISBN: (纸本)9789897585630

Structural characteristics representation and their fine variations are crucial for the recognition of different types of aircrafts in remote sensing images. Aircraft type classification across different sensor remote sensing images by spectral and spatial resolutions of objects in an image involves variable length spatial pattern identification. In our proposed approach, we explore dynamic kernels to deal with variable length spatial patterns of aircrafts in remote sensing images. A Gaussian mixture model (GMM), namely, structure model (SM) is trained over aircraft scenes to implicitly learn the local structures using the spatial scale-invariant feature transform (SIFT) features. The statistics of SM are used to design dynamic kernel, namely, mean interval kernel (MIK) to deal with the spatial changes globally in the identical scene and preserve the similarities in local spatial structures. The efficacy of the proposed method is demonstrated on the multi-type aircraft remote sensing images (MTARSI) benchmark dataset (20 distinct kinds of aircraft) using MIK. Also, we compare the performance of the proposed approach with other dynamic kernels, such as supervector kernel (SVK) and intermediate matching kernel (IMK).

关键词： remote sensing images Aircraft Type recognition Structural Information Model Scale-invariant Feature Transform (SIFT) Dynamic Kernels

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Forest canopy information extraction in Gannan Plateau based on UAV high-resolution images

Forest canopy information extraction in Gannan Plateau based...

引用

2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Wu, Yanduo Han, Hui Faculty of Geomatics Lanzhou Jiaotong University Lanzhou730070 China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou730070 China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou730070 China

ISBN: (纸本)9781510666351

In order to explore the feasibility of applying UAV remote sensing and object-oriented canopy extraction technology to forest areas with different canopy densities, 26 forest plots located in Gannan Plateau were selected and divided into two categories of coniferous forests with medium and high canopy densities. ESP tool was used to screen the optimal segmentation scale for multi-scale segmentation, and the feature space was optimized for object-oriented classification. The east-west and north-south crown amplitudes on quadrat scale were extracted in ArcGIS. The extracted crown amplitude was compared with the field measured data to evaluate the accuracy. The results were as follows: In UAV image extraction, the fitting values R2 and RMSE of east and west canopy width and measured values were 0.865 and 1.025m, respectively, and the fitting values R2 and RMSE of north and south canopy width were 0.867 and 1.235m, respectively. The R2 and RMSE of east and west canopy width with high canopy closure were 0.828 and 1.145m, respectively, and the R2 and RMSE of north and south canopy width were 0.787 and 1.523m, respectively. The extraction effect of the plots with medium canopy closure was better than that of the plots with high canopy closure, and both of the two canopy closure met the accuracy requirements. The results show that the automatic extraction method of canopy parameter information based on UAV remote sensing image is efficient and reliable, and has important practical significance in forest resources investigation. © 2023 SPIE.

关键词： remote sensing

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3D remote Scene Reconstruction via Graph Convolution 10

3D Remote Scene Reconstruction via Graph Convolution

引用

10th International conference on Virtual Reality, ICVR 2024

作者： Li, Xing Sun, Mingyu Ou, Qiaofeng Mo, Yan Liu, Sikun Rao, Zhibo Nanchang Hangkong University School of Information Engineering Nanchang China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350364231

3D urban scene reconstruction is a significant remote sensing topic that provides enriched context on 3D spatial information. However, obtaining a watertight, lightweight, and detailed mesh object from remote sensing images is challenging due to sparse point clouds and inaccurate normals. This paper introduces a 3D mesh reconstruction framework to recover 3D scene information from pairwise remote sensing images, comprising three main stages. Firstly, our previous multi-task network (BGA-Net) is employed to predict the disparity maps from pairwise remote sensing images. Next, we utilize camera parameters and interpolation to reconstruct and fill point clouds, mitigating sparse point clouds (e.g., building edges or unsmooth areas). Finally, a graph convolution network (GCN) is adopted to recover the mesh object from the filled point clouds, reducing the dependency on precise normals. Extensive experiments demonstrate that our framework can qualitatively produce watertight, lightweight, and detailed mesh models. © 2024 IEEE.

关键词： Mesh generation

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Prompt-RSVQA: Prompting visual context to a language model for remote sensing Visual Question Answering

Prompt-RSVQA: Prompting visual context to a language model f...

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

作者： Chappuis, Christel Zermatten, Valerie Lobry, Sylvain Le Saux, Bertrand Tuia, Devis EPFL Sion Switzerland Univ Paris Cite F-75006 Paris France European Space Agcy Lab Frascati Italy

ISBN: (纸本)9781665487399

remote sensing visual question answering (RSVQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in natural language. The traditional vision/language interface is an embedding obtained by fusing features from two deep models, one processing the image and another the question. Despite the success of early VQA models, it remains difficult to control the adequacy of the visual information extracted by its deep model, which should act as a context regularizing the work of the language model. We propose to extract this context information with a visual model, convert it to text and inject it, i.e. prompt it, into a language model. The language model is therefore responsible to process the question with the visual context, and extract features, which are useful to find the answer. We study the effect of prompting with respect to a black-box visual extractor and discuss the importance of training a visual model producing accurate context.

关键词： Training Visualization Natural languages Feature extraction Transformers Question answering (information retrieval) Data mining

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A Deep Neural Network Supported by Ground Verification Data for Active Fire Detection in MODIS

A Deep Neural Network Supported by Ground Verification Data ...

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2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Chen, Jinpeng Xie, Feifei Zheng, Chunkai College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510666351

At present, the use of MODIS to detect fires mainly uses a series of thresholds to identify fire. However, in areas with high heterogeneity, the division of thresholds is more difficult, resulting in many false detections of fire, and cold fire are easily missed. The emergence of deep learning technology has improved the classification accuracy of images, but the surface information of hotspots cannot be determined only by visual interpretation of MODIS images, and the acquisition of high-quality fire samples further limits the detection performance of deep learning methods. In order to solve the above problems, this paper proposes a deep neural network fire detection method based on ground truth data. First, the data structure of the US ground wildfire dataset is studied, a spatiotemporal matching strategy between ground data and remote sensing images is designed, and a priori sample is constructed. library. Second, robust classification features are selected combining the fire physical properties and the spectral distribution of the dataset. Finally, a DNN (Deep Neural Network) for MODIS fire detection is constructed according to the image characteristics. Application experiments were carried out in three typical scenarios and compared with MODIS fire products. The results show that the improved method improves the temporal and spatial adaptability of fire detection, and the extraction of small fire in farming areas is better than MODIS fire products;especially in the suburbs Significant reduction of spurious fire in region extraction, false positives decreased by 19.8%. This method provides a new reference for coarse resolution satellite data to detect fires, and effectively improves the efficiency of fire detection. © 2023 SPIE.

关键词： remote sensing

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Knowledge Based Classifier and pattern recognition Technique for Satellite image Analysis 3

Knowledge Based Classifier and Pattern Recognition Technique...

引用

3rd IEEE Global conference for Advancement in Technology, GCAT 2022

作者： Nimbarte, Nita Nagpure, Aniket Sanodiya, Badal Sevatkar, Harshal Balamwar, Sanjay Yeshwantrao Chavan College of Engineering Electronics & Telecommunication Department Nagpur India Vnit Campus Maharashtra Remote Sensing Application & Centre Nagpur India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

pattern recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis. © 2022 IEEE.

关键词： remote sensing

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HR_BMNet: A boundary perception and multiresolution fusion network for semantic segmentation of remote sensing

HR_BMNet: A boundary perception and multiresolution fusion n...

引用

2024 International conference on image processing and Artificial Intelligence, ICIPAl 2024

作者： Chen, Yan Wang, Mengyuan Jiang, Wenxiang Kang, Menglei Wang, Xiaofeng Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China

ISBN: (纸本)9781510681514

The conventional approach for semantic segmentation of remote sensing imagery using encoder-decoder convolutional neural networks relies on the output of prior feature maps sequentially without considering the interactions between neighboring contextual feature maps with multiple resolutions. While the standard HRNet proposal has successfully improved multi-resolution semantic and spatial features to address the aforementioned issues, its lack of emphasis on boundary perception often results in inadequate target segmentation. Furthermore, a frequent occurrence of multiresolution contextual interaction in HRNet leads to the addition of a significant quantity of redundant information and amplifies the complexity of the model. Hence, to tackle the abovementioned issues, we propose a semantic segmentation network identified as HR-BMNet, which incorporates boundary sensitivity and multiple-resolution learning. The idea associated with standard HRNet is adopted as the foundational architecture. We extend novel boundary perception and multi-resolution fusion attention modules, integrating channel attention mechanisms. The strategy provides an ex-tensive optimization of edges and the efficient capture of crucial multi-scale features. During the feature combination stage, the boundary insights are employed to augment the semantic information, thereby mitigating the spatial details loss, enhancing the intra-class semantic consistency, and achieving superior segmentation. The efficacy of the proposed method is validated through comparison and ablation experiments conducted on the ISPRS Vaihingen and CSRSD datasets. Among the experiments conducted, the best ones attained a mean Intersection over Union (mIoU) of 72.11% on the Vaihingen dataset and 89.28% on the CSRSD dataset, respectively. © 2024 SPIE.

关键词： Semantic Segmentation

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad image processing

Multi-Plant Leaf Disease Detection and Classification using ...

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2024 IEEE International conference on Recent Advances in Science and Engineering Technology, ICRASET 2024

作者： Aishwarya, S.S. Ravikumar, Pushpa Chaithra, I.V. Sagar, Deeksha AIT Adichunchanagiri Institute of Technology Karnataka Chikmagalur India AIT Adichunchanagiri Institute of Technology Department of Computer Science and Engineering Karnataka Chikmagalur India

ISBN: (纸本)9798350388602

Every nation's basic need is the agricultural products. Disease-ridden leaves have an effect on the nation's agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are 20636 photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, 12 are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing;specifically, we achieved 98.29% accuracy in training and 98.029% accuracy in testing across all utilized data sets. pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision. © 2024 IEEE.

关键词： Convolutional neural networks

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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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