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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71-80 订阅

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Implementation of the image super-resolution DWT based algorithm on Raspberry Pi platform for real-time applications

Implementation of the image super-resolution DWT based algor...

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conference on Real-Time processing of image, Depth, and Video Information

作者： Osorno-Ortiz, Raul J. Ponomaryov, Volodymyr, I Reyes-Reyes, Rogelio Cruz-Ramos, Clara Garcia-Salgado, Beatriz P. Sadovnychiy, Sergiy Inst Politecn Nacl ESIME Culhuacan Ciudad De Mexico Mexico Inst Mexicano Petr Ciudad De Mexico Mexico

ISBN: (纸本)9781510673199;9781510673182

Super resolution (SR) is a technique designed for increasing the spatial resolution in an image from a low resolution (LR) to high resolution (HR) size. SR technology has had a considerable demand in a wide variety of applications to recover HR images, such as medicine, engineering, computer vision, pattern recognition and video production, etc. In contrast to interpolation-based algorithms that often introduce distortions or irregular borders, this study proposes an implementation that can preserve the edges and fine details of an original image through the computation of the wavelet decomposition. Different Discrete Wavelet Transform (DWT) families such as: Daubechies, Symlet, and Coiflet were evaluated. The proposed system was implemented on a Raspberry Pi 4 model B, an embedded device, to get around the PC's mobility limitations, making it possible to create an in-expensive and energy- efficient SR system, reducing their complexity in realtime applications. To investigate the visual performance, SR images have been analysed in subjective matter via human perception view, guaranteeing good perception for the images of different nature from three different datasets such as FullHD (DIV2K), medical (Raabin WBC), and remote sensing (Sentinel- 1). The experimental results of designed implementations appear to demonstrate good performance in commonly used objective criteria: execution time, SSIM, and PSNR (0.742 sec., 0.9164, and 38.72 dB), respectively for images with a super resolution size of 1356 x 2040 pixels.

关键词： Super resolution Discrete Wavelet Transform (DWT) Parallel programing Raspberry Pi real-time processing

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Hyperspectral image classification based on spectral feature extraction

Hyperspectral image classification based on spectral feature...

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

作者： Wang, Li Wang, Wei Department of Electronic Engineering Xi'an Aeronautical Institute Shaanxi Xi'an710077 China

ISBN: (纸本)9781510666351

Hyperspectral remote sensing can synchronously obtain the surface coverage space image and continuous spectral data, and can realize fine classification and recognition of ground objects. The motivation for this paper is to search an effective specteal feature extraction method for hyperspectral classification. The main methods used to extract spectral features of hyperspectral images are principal component analysis (PCA) and linear discriminant analysis (LDA). The realization of classification based on k-nearest neighbor (KNN) classifier is discussed. The training set and test set of Pavia University, Indian Pines and Salinas are selected as the data source, and the selection experiment of algorithm parameters is set, and the classification performance under different feature extraction methods is compared and analyzed. The experimental results show that with the parameters of OA, PA and Kappa based on the confusion matrix as the evaluation indicators, the classification accuracy based on LDA method is higher, and the effective classification of ground objects can be achieved. The results could provide a basis for further research on hyperspectral image classification based on spatial spectral feature extraction. © 2023 SPIE.

关键词： Principal component analysis

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Applying Computational Topology for Enhanced image recognition and Computer Vision 2

Applying Computational Topology for Enhanced Image Recogniti...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Siddhan, Saravanan Moorthy, A. Christy, S. Sivasankar, C. Bhaskar, K. Vijaya Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Saveetha School of Engineering SIMATS Saveetha University Thandalam Tamil Nadu Chennai India Tamilnadu Chennai India

ISBN: (纸本)9798350383867

Computational topology has consequently shorten the time taken for image recognition with good accuracy and therefore has boosted the performance of computer vision. This paper uses computational topology in different domains like society, livestock, medicine, and remote-sensing. Two primary outcomes have been revealed in the experiment: tightly-spaced pictures, thanks to topology, have increased accuracy by up to 5.0 percent compared to conventional facilitation. Considering the topology-enhanced method edge processing time, the calculation time will increase slightly, but it will have a significant performance advantage. Additionally, the edge, texture, shape, and pattern detection are contributed to the overall classifier output, and the performed method reaches the rate of accuracy of entities detection up to the prior methods. Primarily, we develop and compare the two methods with each other on the basis of accuracy, computational cost, scalability, noise resistance. The grading results show that the improved technique influences picture identification and computer vision system, making the technologies more mature and precise to be used on the real-world tasks. © 2024 IEEE.

关键词： Machine vision

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Unsupervised Cross-Domain Network Based on Bi-classifier for PolSAR image Classification 6

Unsupervised Cross-Domain Network Based on Bi-classifier for...

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6th International conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Hua, Wenqiang Liu, Lin Sun, Nan Wang, Chen Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is an important research content in the field of remote sensing. However, the lack of effective labeled data and the serious class distribution shift problem between different datasets hinder the application of deep learning networks in PolSAR image classification. Therefore, this paper proposes a Bi-classifier adversarial network based on covariance difference matrix and Convolutional Block Attention Module (CBAM) to solve the cross-domain problem in PolSAR images. Firstly, since PolSAR image processing generally uses the covariance matrix or coherence matrix of a single target pixel to construct the data set, which cannot effectively use the local information of PolSAR data,Therefore, the PolSAR coherence difference matrix P is proposed as the description of the PolSAR image difference information. Secondly, due to the different importance of features between neighboring pixels, the CBAM mechanism is introduced in order to exploit the spatial information of PolSAR data. Finally, in order to solve the cross-domain problem of PolSAR images more effectively, a bi-classifier adversarial network based on covariance difference matrix and CBAM is proposed. In this paper, we conduct experiments on public datasets, using images of San Franciscoii acquired in 2008 as the source domain, and images of San Francisco ii acquired in 1992 as the target domain data. The experimental results show that the proposed method can effectively solve the cross-domain problem of PolSAR images. © 2023 ACM.

关键词： image classification

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Deep learning landslide extraction based on multi-temporal image difference local spatial information

Deep learning landslide extraction based on multi-temporal i...

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

作者： Miao, Sheng Qu, Yu Liu, Xirong College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781510666351

Landslide as a natural disaster, it is important to obtain accurate information of landslide spatial distribution. The current method of landslide extraction is mainly based on the spectral features, spatial structure and texture features of landslides on remote sensing images, which is difficult to accurately extract the spatial distribution of landslides for surface structures with similar spectral features (bare soil, villages, exposed rocks, roads, water bodies, etc.) and does not make full use of local spatial information features and causes loss of landslide boundary information. To address the above problems, this paper proposes a method to extract landslides based on the fusion of local spatial information with multi-temporal image differences. The high-resolution images before and after the landslide are used to calculate the difference images, extract each band and combine the temporal difference images to combine the bands and use them as input features for deep learning. The ASPP module is introduced to the U-net network model for expanding the local spatial information perception field, enhancing the preservation of landslide boundary information, and guiding the attention mechanism of feature downsampling for extracting multi-scale contextual information. The evaluation shows that the method can ensure the detection error of landslide area range is less than 8.6%, the extraction accuracy is more than 95%, and the Kappa is 0.90. The experimental results prove the feasibility and accuracy of the method in rainfall landslide detection. © 2023 SPIE.

关键词： Landslides

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Deep density estimation based on multi-spectral remote sensing data for in-field crop yield forecasting

Deep density estimation based on multi-spectral remote sensi...

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

作者： Baghdasaryan, Liana Melikbekyan, Razmik Dolmajain, Arthur Hobbs, Jennifer Intelinair Inc Yerevan Armenia Intelinair Inc Chicago IL USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Yield forecasting has been a central task in computational agriculture because of its impact on agricultural management from the individual farmer to the government level. With advances in remote sensing technology, computational processing power, and machine learning, the ability to forecast yield has improved substantially over the past years. However, most previous work has been done leveraging low-resolution satellite imagery and forecasting yield at the region, county, or occasionally farm-level. In this work, we use high-resolution aerial imagery and output from high-precision harvesters to predict in-field harvest values for corn-raising farms in the US Midwest. By using the harvester information, we are able to cast the problem of yield-forecasting as a density estimation problem and predict a harvest rate, in bushels/acre, at every pixel in the field image. This approach provides the farmer with a detailed view of which areas of the farm may be performing poorly so he can make the appropriate management decisions in addition to providing an improved prediction of total yield. We evaluate both traditional machine learning approaches with hand-crafted features alongside deep learning methods. We demonstrate the superiority of our pixel-level approach based on an encoder-decoder framework which produces a 5.41% MAPE at the field-level.

关键词： Deep learning Satellites conferences Government Estimation Crops pattern recognition

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4th International conference on Advances in Computational Science and Engineering, ICACSE 2023

4th International Conference on Advances in Computational Sc...

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4th International conference on Advances in Computational Science and Engineering, ICACSE 2023

ISBN: (纸本)9789819729760

The proceedings contain 52 papers. The special focus in this conference is on Advances in Computational Science and Engineering. The topics include: Sensitivity Analysis on Absorber Column as Hydrogen Gas Purification Unit Using Aspen Plus;use that Pinky on the ‘A’ Key: A Finger-Key Identification Module for a Touch Typing Trainer;Accelerating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Using Vincenty’s Inverse Method on CUDA;procedural Modeling for Sustainable Urban Development and Planning: A Blender Plugin for 3D Modeling of Philippine Cities;estimating Orangutans Population Size in Sabah Rainforest in Malaysia Using Spatial Modelling;multilayer Gaussian Feature Extraction Algorithm for Sky image Classification;face Expression recognition: A Survey on Hyperparameter Optimization;Enhancing Low Light image Classification Using MADPIP Approach;a Robust Multiple Adaptive Derivative Face recognition System on Pose and Illumination;performance Comparison of Convolutional Neural Network Deep Learning Architectures for remote sensing image Segmentation;revolutionizing Human–Computer Interaction: Unraveling the Power of Deep Learning Convolutional Neural Networks in Face recognition;Multiple Adaptive Derivative Passive image processing Approach to Unsharp Masking Restoration for CNN on Low Light images;data-Driven Insights for Strengthening Information Security Awareness in Higher Education Institutions;Determining Motivational Factors for Retention and Course Completion Among Filipino MOOC Learners: A Thematic Analysis;development of Kansei-Based Visualization pattern for E-Learning Website;augmented Reality in Education: Transformative Innovations and Immersive Learning Experiences;classification of Game Mechanics: A Brief Review;challenges and Issues in Team Gamer Loyalty for Massively Multiplayer Online Game;optimizing Cloud-Based Educational Services for Enhanced Learning in Higher Education Institutions;biomarker Identification for Lung Canc

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Railway facilities safety monitoring system based on space-air-ground multi-source data

Railway facilities safety monitoring system based on space-a...

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

作者： Xue, Aliang Guo, Hang Zhang, Na Miao, Long China Coal Aerial Remote Sensing Group Co. Ltd. 216 Shenzhou Fourth Road Xi'an China Xi'an Rail Transit Group Co. Ltd. 126 Fengcheng 8th Road Xi'an China

ISBN: (纸本)9781510666351

In view of many problems existing in the current railway transportation process, such as pedestrian crossing the guardrail, landslide, rock rolling, debris flow and other factors, this paper summarizes the research status of the underground railway foreign body intrusion monitoring system at home and abroad, and puts forward a railway facility safety monitoring system based on multi-source data from the sky and the sky. The system uses satellite, unmanned aerial vehicle, distributed optical fiber sensor, video, GNSS and GIS technologies to realize the safety monitoring of railway facilities, and formulates the technical scheme for the safety information management of space-based railway facilities, and tests and evaluates the research results through the test section. The development trend of railway facilities safety monitoring system is prospected as follows: foreign body intrusion monitoring at the boundary of key sections;Real-time monitoring of tunnel entrance and high slope section stones;Carry out dynamic monitoring of off-road safety environment. © 2023 SPIE.

关键词： Optical fibers

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Satellite image Classification Using ANN 1st

Satellite Image Classification Using ANN

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1st International conference on Cyber Warfare, Security and Space Research, SpacSec 2021

作者： Mathur, Pratistha Kavita Manipal University Jaipur Jaipur India

ISBN: (纸本)9783031157837

Every human being is capable of recognizing patterns. There are various problems in domains like bioinformatics, data mining, document classification, image analysis, remote sensing, and speech recognition where automatic recognition is needed. The rapid growth in computing power enables the development of such applications. remote sensing is one of the areas where a huge amount of data is available. In this paper, we have explored the supervised and unsupervised ANN for classifying satellite images. The adaptive pattern recognition capability of ANN is used to simulate the spatial variations in the images of an object/area. Multi-layer feed-forward neural network using backpropagation learning method and self-organizing feature map (SOFM) has experimented. Experiments are implemented using MATLAB. It is hoped that this multidisciplinary work will be useful for researchers and image processing users in the field of computer science and remote sensing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MATLAB

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Realization Technology of Texture Feature Extraction Algorithm of remote sensing Satellite image Based on FPGA 2

Realization Technology of Texture Feature Extraction Algorit...

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2nd IEEE International conference on Data Science and Computer Application, ICDSCA 2022

作者： Feng, Kai Zhang, Rui Shaanxi Academy of Aerospace Technology Application Co. Ltd. Xi'an China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

In recent years, the research on remote sensing image information output has developed rapidly, including not only remote sensing technology, image processing, pattern recognition, etc., but also land use, environmental monitoring, disaster forecasting, urban planning and other fields. The purpose of this paper is to study the realization technology of remote sensing satellite image texture feature extraction algorithm based on FPGA. A hardware system implementation scheme based on FPGA is proposed. Firstly, the overall structure of the system is established, and the software and hardware platforms are selected. Then, according to the difficulty of the algorithm, it is proposed to divide the algorithm into two parts, and the system based on hardware logic and based on soft core processor is described in detail. Implementation plan. An object modeling method based on Gabor texture blocks is introduced. This method is applied to the extraction of urban buildings from remote sensing images, and the final building extraction accuracy reaches 91.5%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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