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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

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CDCEO'21-First Workshop on Complex Data Challenges in Earth Observation 21

CDCEO'21-First Workshop on Complex Data Challenges in Earth ...

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30th ACM International conference on Information and Knowledge Management (CIKM)

作者： Gruca, Aleksandra Herruzo, Pedro Ripodas, Pilar Kucik, Andrzej Briese, Christian Kopp, Michael K. Hochreiter, Sepp Ghamisi, Pedram Kreil, David P. Silesian Tech Univ Gliwice Poland Inst Adv Res Artificial Intelligence Vienna Austria Agencia Estatal Meteorol AEMET Madrid Spain European Space Agcy ESRIN Frascati Italy EODC Earth Observat Data Ctr Water Resources Moni Vienna Austria Helmholtz Zentrum Dresden Rossendorf Dresden Germany

ISBN: (纸本)9781450384469

High-resolution remote sensing technology for Earth Observation (EO) has radically changed how we monitor the state of our planet around the clock. An effective interpretation of the resulting complex large-scale time series adopts the best machine learning techniques from signal processing, computer vision, pattern recognition, and artificial intelligence. The First Workshop on Complex Data Challenges in Earth Observation was open to both method development and advanced applications in a wide range of related topics, including image and signal processing, gap-filling, data fusion, feature extraction, prediction of spatio-temporal features, and the detection of rules underlying the observed state transitions and causal relationships. The full agenda, featuring keynotes and a selection of high quality contributed talks is available online at ***/cdceo21.

关键词： earth observation machine learning remote sensing weather

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3rd International conference on 3D Imaging Technologies - Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2021

3rd International conference on 3D Imaging Technologies - Mu...

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3rd International conference on 3D Imaging Technologies - Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2021

ISBN: (纸本)9789811924514

The proceedings contain 23 papers. The special focus in this conference is on 3D Imaging Technologies - Multidimensional signal processing and Deep Learning. The topics include: Prediction of Silicon Content in Molten Iron Based on EMD-GA-LSTM;using Meta Path Information to Adjust Embedded Recommendation System;intelligent Campus Blog Preprocessing Model;an Experimental Study on Extraction Method of Tobacco Color Distribution Feature;research on the Distribution Map of Weeds in Rice Field Based on SegNet;preface;study on remote sensing Retrieval of Land Surface Evapotranspiration in Poyang Lake Region in Typical Hydrological Year;research on the Application of Artificial Intelligence in Unstructured Data Resource Management of Power News Information;analysis and Application of Medical images in the Field of Artificial Intelligence;development and Design of Surface Quality Online Inspection System Based on Machine Vision;terahertz Surface-Plasmon-Polaritons Gradient Index Lens;Improvement of YOLOv4 Algorithm and Its Application in Objective Identification of Railway Level Crossing;design of Automatic Verification System for Electronic Balance Based on Machine Vision;research on Interactive Art Design System Based on Artificial Intelligence Technology;infrared Imaging Circuit Multi-Channel Analog-To-Digital Sampling Circuit Inconsistency Correction;research on Artificial Intelligence System Design of Ideological and Political Education;research on 3D Virtual Emotional Interaction Design Based on Network Digitization;research on image Quality Assessment of Vehicle Panoramic View Equipment;research and Implementation of Digital Art Media System Based on Big Data Aesthetics;Research on Application of OpenGL-Based Game Design;application of Artificial Intelligence Technology in Marketing.

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remote sensing satellite rapid response technology based on orbit maneuver 7th

Remote sensing satellite rapid response technology based on ...

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7th International conference on signal and Information processing, Networking and Computers, ICSINC 2020

作者： Zhang, Ran Li, Xiaojuan Yu, Junhui Huang, Yufei Institute of Spacecraft System Engineering CAST Beijing China

ISBN: (纸本)9789813341012

remote sensing satellite is an important means to acquire image information of key regions in order to meet the emergency needs of natural disasters like earthquake and wildfire. Limited by the orbit properties, the response time of the remote sensing satellite to the emergency mission is uncertain, and the revisit interval is long, which does not meet the rapid-response and frequent-visit requirement. In this paper, an orbit maneuver method is proposed, by adjusting the semi-major axis of the remote sensing satellite and changing the sub-satellite points, the satellite can respond to the emergency mission within 24 h and shorten the interval of the revisit to one day. Simulation results show that the proposed method is simple and effective, and can meet the requirements of emergency missions. Fuel consumed of a typical satellite for a specific mission is about 50 kg. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Satellites

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Relation Aware Network for Multi-View SAR Target Recognition

Relation Aware Network for Multi-View SAR Target Recognition

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Yijun Tang Ling Wang Yong Li Daiyin Zhu Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture Radar (SAR) has unique advantages and plays an important role in the field of remote sensing, which makes the research of SAR Automatic Target Recognition (ATR) very hot. In recent years, due to the rapid development of deep learning networks and hardware computing power, the deep learning based ATR algorithm for SAR has gradually become the mainstream. Compared with the single-view based recognition, multi-view SAR target recognition tends to better utilize the target scattering information of different azimuth angles. In this paper, we propose a Multi-View Relation Aware (MVRA) network for SAR ATR, which can capture the latent relationships between multiple input views, achieving better recognition accuracy than single-view recognition. Additionally, to enable the network to more accurately capture the contour information of SAR image targets, the Laplacian pyramid is applied for SAR image preprocessing. The proposed MVRA network shows a significant improvement in recognition accuracy on the public dataset MSTAR compared to other multi-view SAR target recognition algorithms. Furthermore, on the new Ground Military Vehicle Target (GMVT) dataset constructed by Nanjing University of Aeronautics and Astronautics (NUAA), the proposed MVRA network demonstrates excellent generalization performance even in the scenarios with relatively strong background clutter interference.

关键词： Deep learning Accuracy Laplace equations image recognition Target recognition Feature extraction Radar polarimetry Clutter Synthetic aperture radar Testing

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Wild Animal Detection using a Machine Learning Approach and Alerting using LoRa Communication

Wild Animal Detection using a Machine Learning Approach and ...

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2022 International conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Bandari, Gnaneshwar Nirmala Devi, Prof. L Srividya, P. University College of Engineering Osmania University Me - Systems and Signal Processing Ece Dept. Hyderabad India University College of Engineering Osmania University Ece Department Hyderabad India University College of Engineering Osmania University Ece Department Hyderabad India

ISBN: (纸本)9781665454995

It is crucial to effectively and consistently monitor wild animals in the vicinity of the forest boundaries. In this paper, an algorithm is demonstrated to identify wild animals in order to protect them. Since there are so many different kinds of animals, manually recognizing them might be challenging. To better effectively monitor animals, this system categorizes them based on their images. Animal monitoring, theft prevention, and animal-vehicle accident prevention can all be aided by animal detection and classification. Applying efficient deep learning techniques can help with this. When an animal is detected, an alert will be sent to another device through LoRa communication because standard communications like WiFi and GSM technologies may not be available in the remote sensing area. Computer vision using Python is utilized for image processing. Raspberry Pi development board is used where the model is deployed and live streaming is processed to detect a wild animal which will differentiate between Humans and Domestic animals. Once, a wild animal is detected an alert is sent through LoRa communication to an end-user who is monitoring the wildlife. © 2022 IEEE.

关键词： Convolutional neural networks

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BUILDING FOOTPRINT GENERATION BY INTEGRATING U-NET WITH DEEPENED SPACE MODULE

BUILDING FOOTPRINT GENERATION BY INTEGRATING U-NET WITH DEEP...

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IEEE International conference on image processing (ICIP)

作者： Chen, Jun Jiang, Yuxuan Luo, Linbo Gu, Yue Wu, Kangle China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665441155

In this paper, we propose a novel and practical convolutional neural network method for building footprint generation in remote sensing images, in order to deal with the problem that the detailed information and geometric structure of ground objects in high-resolution images become more abundant, which leads to a large increase in the calculation amount. So we introduce a deepened space module, which can ignore the channels with weak target features and emphasize the effective features. It is embedded in each splicing layer in the upsampling process of U-net to achieve the effect of feature selection. By means of clipping and data enhancement, we carry out iterative training and model optimization learning on Inria aerial image label dataset, and realize the automatic generation of building footprint. Compared with FCN8s, Unet, SegNet, PSPNet, Deeplabv3+ and GLNet, experimental results show that the method we use to generate building footprint is more accurate, and in IoU, mPA, PA three indicators are better than the comparison algorithms.

关键词： deep learning semantic segmentation high resolution image

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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Complex-valued Neural Network for Hyperspectral Single image Super Resolution 2

Complex-valued Neural Network for Hyperspectral Single Image...

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conference on Hyperspectral Imaging and Applications II

作者： Aburaed, Nour Alkhatib, Mohammed Q. Marshall, Stephen Zabalza, Jaime Al Ahmad, Hussain Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland Univ Dubai Coll Engn & IT Dubai U Arab Emirates

ISBN: (纸本)9781510657489;9781510657496

remote sensing applications are nowadays widely spread in various industrial fields, such as mineral and water exploration, geo-structural mapping, and natural hazards analysis. These applications require that the performance of image processing tasks, such as segmentation, object detection, and classification, to be of high accuracy. This can be achieved with relative ease if the given image has high spatial resolution as well as high spectral resolution. However, due to sensor limitations, spatial and spectral resolutions have an inherently inverse relationship and cannot be achieved simultaneously. Hyperspectral images (HSI) have high spectral resolution, but suffer from low spatial resolution, which hinders utilizing them to their full potential. One of the most widely used approaches to enhance spatial resolution is Single image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been widely used for HSI enhancement, as they have shown superiority over other traditional methods. Nonetheless, researches still aspire to enhance HSI quality further while overcoming common challenges, such as spectral distortions. Research has shown that properties of natural images can be easily captured using complex numbers. However, this has not been thoroughly investigated from the perspective of HSI SISR. In this paper, we propose a variation of a Complex Valued Neural Network (CVNN) architecture for HSI spatial enhancement. The benefits of approaching the problem from a frequency domain perspective will be answered and the proposed network will be compared to its real counterpart and other state-of-the-art approaches. The evaluation and comparison will be recorded qualitatively by visual comparison, and quantitatively using Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Spectral Angle Mapper (SAM). The project can be access at: https://***/NourO93/3D_CCVNN_Hyperspectral.

关键词： Hyperspectral spatial enhancement super resolution single image super resolution complex valued neural networks CNN 3D-CNN

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A Simple Scheme for Coupled Factorization for Hyperspectral Super-Resolution: Exploiting Sparsity in an Easy Way

A Simple Scheme for Coupled Factorization for Hyperspectral ...

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

作者： Yuening Li Wing-Kin Ma Ruiyuan Wu Huikang Liu Department of Electronic Engineering The Chinese University of Hong Kong Meituan Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics

In this paper we develop a simple scheme for a coupled matrix factorization problem arising in the topic of hyperspectral super-resolution (HSR). HSR considers the problem of recovering a super-resolution image from a multispectral image and a hyperspectral image, which have lower spectral and spatial resolutions, respectively, and it is a motivated topic in the domain of remote sensing. The challenge with coupled factorization (COFAC) is that we are required to simultaneously factorize two data matrices, with their factors being interrelated. We adopt a separable COFAC strategy, in which we first factorize one data matrix, and then use the retrieved factors and the coupled factor relationship to help us factorize another matrix; the merit is that it may lead to simple COFAC schemes. Our scheme is based on the simplex-structured factorization model, which is commonly used in HSR, and a sparse factor assumption. In particular, we leverage the coupled factor structure to exploit sparsity in an easy way; we solve simple constrained least squares problems, and we sidestep the need to do sparse optimization. Numerical results show that our proposed scheme works reasonably on both semi-real data and synthetic data, and it runs much faster than some state-of-the-art COFAC schemes.

关键词： Superresolution Numerical models Sparse matrices Data mining Speech processing Spatial resolution signal resolution

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A Two-Stage Line Matching Method for Multi-temporal remote sensing images 6

A Two-Stage Line Matching Method for Multi-temporal Remote S...

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6th International conference on signal and image processing, ICSIP 2021

作者： Wang, Zhengbing Nie, Jianhua Li, Dan Feng, Xugang Wu, Yuxiu Xu, Guili Anhui University of Technology College of Electrical and Information Engineering Ma'anshan China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China

ISBN: (纸本)9780738133737

Robust feature matching in multi-temporal remote sensing images is an essential but difficult task due to certain extent of appearance difference. In this paper, a two-stage line matching method is presented to match salient structures in multi-temporal remote sensing images. The proposed method consists of three main steps. In the first step, we extract the line structures from two corresponding images using the EDLines algorithm, and a line validation procedure is then implemented to remove meaningless and fragmented lines. In the second step, the detected lines are further divided into a set of line segments by a length threshold, and a new descriptor is computed to describe each line segment. After that, a coarse matching process is implemented to compute the projective transformation model. Finally, we adjust the length threshold according to the transformation model, and recompute the descriptors to further refine the matching process. We conduct the experiments on two pairs of multi-temporal remote sensing images, and the results demonstrate that the proposed approach can achieve favorable performance compared to common image matching methods. © 2021 IEEE.

关键词： image matching

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