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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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Global Information Edge Augmentation Network for remote sensing image Continuous Super-Resolution

Global Information Edge Augmentation Network for Remote Sens...

引用

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

作者： Shize Gao Yong Lei Jingya Zhang Guoqing Wang Wenchao Liu National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The continuous-scale super-resolution (SR) method for remote sensing (RS) images has the potential to achieve flexible scale SR with a single network, representing a significant research area in the field of RS. However, the majority of prevailing methods learn features from localization, thereby neglecting the global semantic coherence of RS images. This results in unbalanced SR outcomes. Furthermore, the optimization strategy based on the multilayer perceptron (MLP) utilized in the implicit neural representation (INR) method results in blurred reconstructed image edges. To address these issues, a novel continuous-scale SR method for RS images, a global information edge augmentation network (GIEAN), is proposed. Initially, the global state space model (GSSM) aggregates the global information of the image from diverse perspectives and learns the contextual interactions at disparate locations of the image globally. Subsequently, the dual edge enhancement module (DEEM) learns the image body and edges independently through the main branch and edge branch, respectively, with the objective of enhancing the edge component of the SR results. Extensive experimental evaluation on the UCMerced dataset demonstrates the superiority of GIEAN over existing continuous-scale SR techniques.

关键词： image edge detection Aggregates Superresolution Semantics Coherence Feature extraction Sensors remote sensing image reconstruction Optimization

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Efficient Vision Mamba with Dynamic Feature Fusion for remote sensing image Semantic Segmentation

Efficient Vision Mamba with Dynamic Feature Fusion for Remot...

引用

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

作者： Chenchen Liu Yice Cao Dingkun Jiang Zhenhua Wu He Hu Zhixiang Huang School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The improvement in imaging resolution and the increase in imaging swath of remote sensing satellites have enabled the acquisition of vast and complex remote sensing image data. Effectively and comprehensively extracting target information to enhance segmentation accuracy and interpretation efficiency has become a research hotspot in the field of remote sensing semantic segmentation. Although current segmentation algorithms based on CNN and Transformer frameworks have made significant progress in performance, achieving a good balance between segmentation accuracy and computational complexity remains challenging, limiting their widespread application in practical tasks to a certain extent. To address this issue, this paper introduces a state-space model and proposes an efficient vision Mamba for remote sensing image semantic segmentation, abbreviated as EVMDNet. By designing an efficient vision state space block (EVSSBlock), the method effectively integrates local and non-local features while improving computational efficiency through efficient 2D scanning (ES2D) strategy. Furthermore, to avoid the insufficient utilization of features and weak discriminative ability caused by the direct addition of skip connections, a dynamic feature fusion block (DFFBlock) is designed to adaptively adjust the weights of different features for efficient feature fusion. Experimental results on classical remote sensing image datasets demonstrate that the proposed method not only improves segmentation accuracy but also reduces computational complexity to a certain extent, outperforming existing state-of-the-art segmentation algorithms.

关键词： Accuracy Satellites Semantic segmentation Imaging Transformers Computational efficiency State-space methods Computational complexity remote sensing Optimization

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AN INTERFERENCE SUPPRESSION ALGORITHM BASED ON TIME-FREQUENCY FEATURES

AN INTERFERENCE SUPPRESSION ALGORITHM BASED ON TIME-FREQUENC...

引用

IET International Radar conference 2023, IRC 2023

作者： Duan, Lining Wu, Yaojun Xiao, Ningjie Peng, Benwang Quan, Yinghui School of Electronic Engineering Xidian University Xi'an710071 China Xi'an Key Laboratory of Advanced Remote Sensing Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9781839539954

Interrupted-sampling repeater jamming (ISRJ) creates a cluster of false targets at the radar receiver, significantly degrading the radar's target detection performance. We investigate a method for suppressing ISRJ based on time-frequency features. Initially, we apply de-chirping processing to the echo signal and use time-frequency transformation to obtain the time-frequency characteristics of the target and ISRJ. To mitigate the impact of noise on the extraction of time-frequency characteristics, we perform binarization processing on the image obtained after the time-frequency transformation. Finally, we utilize two sets of features to achieve the extraction of target information and the suppression of ISRJ. Simulation experiments demonstrate the effectiveness of the proposed algorithm in suppressing interference. © The Institution of Engineering & Technology 2023.

关键词： Radar receivers

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Wide Area Lidar Locator (WALL) 29

Wide Area Lidar Locator (WALL)

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conference on Laser Radar Technology and Applications XXix

作者： Lundquist, Paul Teta, Andrew Arete 6450 Dry Creek Pkwy Niwot CO 80503 USA

ISBN: (纸本)9781510674172;9781510674165

Arete has developed and demonstrated a LIDAR sensing technology for detecting and locating distant small objects near the horizon with rapid 360-degree scans using low-cost components. This technology has been demonstrated for detection of sUAS. The technology utilizes a coordinated high-speed, spinning, scanner and imaging receiver to obtain range-angle-angle measurements within large solid angles. Ranging is achieved by angular displacements of images due to receiver angular rotation during the time of flight from the sensor to a target and back. Because ranging is not achieved by laser modulation, the laser can be chosen as a continuous wave laser enabling much greater flexibility in system design for greater efficiency and lower cost. The sensor provides continuous coverage without gaps and has been demonstrated in detection while scanning. Instead of using custom high-bandwidth detectors, ranging is measured in the spatial domain at a camera image plane. The sensor technology has no range ambiguity constraints as typically result in high pulse-rate LIDAR systems often used for rapid large area coverage. Arete has demonstrated a bistatic version of the sensing approach with separate transmitter and receiver scanners sharing a common rotational axis and with an event camera imager. An event camera at the image plane, and appropriate signal processing, reduces sensitivity to solar background. Because of the technologies compatibility with narrow linewidth CW lasers, it is expected to also have additional utility for detection of chemical agent dispersals as well as low-SWaP vertical profiling DiAL systems for atmospheric monitoring.

关键词： Lidar cUAS detection remote detection event-camera DiAL

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Towards Deep Reference Frame in Versatile Video Coding NNVC

Towards Deep Reference Frame in Versatile Video Coding NNVC

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2023 IEEE International conference on Visual Communications and image processing, VCIP 2023

作者： Bao, Weijie Jia, Jianghao Meng, Wenhui Liu, Zizheng Xu, Xiaozhong Liu, Shan Chen, Zhenzhong Wuhan University School of Remote Sensing and Information Engineering Wuhan China Tencent Media Lab Shenzhen China Tencent Media Lab Palo AltoCA United States

ISBN: (纸本)9798350359855

In this paper, we propose a deep reference frame generation method that aims to enhance bi-direction inter prediction under random access configuration in the latest video coding standard, Versatile Video Coding. Specifically, a pair of neighboring reconstructed frames are selected from decoded picture buffer and put into an optical-flow-based interpolation network to synthesize a new frame, similar to the current to-be-coded frame. Subsequently, this synthesized frame is incorporated into two-sided picture reference lists as additional reference frames. The proposed method is employed in both the encoding and decoding processes to eliminate bitstream signaling for supplementary information. The Small Ad-hoc Deep-Learning Library is utilized for implementing the proposed method. Experimental results demonstrate 3.67%/7.34%/6.51% coding efficiency improvements for Y/U/V components under the random access configuration when compared to the Versatile Video Coding NNVC reference software VTM-11_NNVC-5.0. © 2023 IEEE.

关键词： Video signal processing

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AI4EO HYPERVIEW: A SPECTRALNET3D AND RNNPLUS APPROACH FOR SUSTAINABLE SOIL PARAMETER ESTIMATION ON HYPERSPECTRAL image DATA 29

AI4EO HYPERVIEW: A SPECTRALNET3D AND RNNPLUS APPROACH FOR SU...

引用

IEEE International conference on image processing (ICIP)

作者： Zelenka, Claudius Lohrer, Andreas Bayer, Mirjam Kroeger, Peer Univ Kiel Kiel Germany Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The goal of the #Hyperview challenge is to use Hyperspectral Imaging (HSI) to predict the soil parameters potassium (K), phosphorus pentoxide (P2O5), magnesium (Mg) and the pH value. These are relevant parameters to determine the need of fertilization in agriculture. With this knowledge, fertilizers can be applied in a targeted way rather than in a prophylactic way which is the current procedure of choice. In this context we introduce two different approaches to solve this regression task based on 3D CNNs with Huber loss regression (SpectralNet3D) and on 1D RNNs. Both methods show distinct advantages with a peak challenge metric score of 0.808 on provided validation data.

关键词： 3D Convolutional Neural Net (3DCNN) Recurrent Neural Network (RNN) Regression Soil Parameter Prediction remote sensing

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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...

引用

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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A GNSS-IR Aided Multispectral Satellite Data Fusion for Meter-Level Wide-Area Volumetric Soil Moisture Estimation

A GNSS-IR Aided Multispectral Satellite Data Fusion for Mete...

引用

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

作者： Nicolás Padrón Sergiy Vorobyov School of Electrical Engineering Aalto University Espoo Finland Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Earth Observation (EO) data is captured with different instruments and available in multiple formats. The complementation of two passive remote sensing approaches in local small areas is performed here to produce a single, large-area coverage, volumetric soil moisture (VSM) solution. The sensing approaches under consideration are GNSS Interferometric Reflectometry (GNSS-IR), which is a local land-based microwave remote sensing approach that exploits ground-reflected navigation signals; and also multispectral satellite imagery, which is a space-based optical remote sensing approach that allows to analyze the spectral response of surface materials. The focus is on the processing of GNSS-IR outputs aiding a multispectral model using Landsat-8 data. The aim is to provide an accurate, cost-effective solution with wide-area coverage. Landsat-8 spectral indexes highly correlated with soil moisture are fused with GNSS-IR VSM on multiple terrain types. The proposed solution is demonstrated and verified against data from the Soil Moisture Active Passive (SMAP) satellite mission over large-areas.

关键词： Earth Optical interferometry Global navigation satellite system Artificial satellites Satellites image resolution Soil moisture Estimation remote sensing signal resolution

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Scale Matching Based remote sensing image Cloud Detection in Southwest Mountainous Areas

Scale Matching Based Remote Sensing Image Cloud Detection in...

引用

2022 International conference on signal processing and Communication Security, ICSPCS 2022

作者： Dong, Lulu Chen, Yu Ke, Nan Tu, Wenli Zhang, Xin Dong, Wen Su, Xiaojie School of Earth Science and Engineering Hebei University of Engineering Handan China The Center of Agriculture Information of Chongqing Chongqing China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China College of Surveying and Geographic Informatics Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9781510660151

Sample quality is the key to automated cloud detection from regional remote sensing images, and scale is one of the major impediments to sample quality control. In this paper, we select the southwest mountainous area in China, which is fragmented, cloudy, and rainy, as the study area. We proposed a method for constructing a cloud detection dataset based on the idea of downscaling and the spectral characteristics of vegetation. Finally, we validated the dataset by the U-Net+ deep learning model. The experimental results show that the cloud detection accuracy reaches 95.11% when using the dataset constructed in this paper, which is approximately 40% higher than the cloud detection accuracy with large-scale samples. Additionally, it reduced the workload of masking a large number of samples for a specific region and realizing the possibility of efficient cloud detection in the region. © 2022 SPIE.

关键词： remote sensing

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AN OVERVIEW OF THE CONTRIBUTIONS OF JOSE MANUEL BIOUCAS-DIAS TO remote sensing image processing

AN OVERVIEW OF THE CONTRIBUTIONS OF JOSE MANUEL BIOUCAS-DIAS...

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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Plazas, Antonio Li, Jun Figueiredo, Mario A. T. Univ Extremadura Hyperspectral Comp Lab Caceres Spain Sun Yat Sen Univ Sch Geog & Planning Guangzhou Peoples R China Univ Lisbon Inst Telecomunicacoes Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9781665403696

Jose Manuel Bioucas-Dias was an outstanding expert in many different IEEE-related areas, including inverse problems in imaging, signal and image processing, pattern recognition, optimization, and remote sensing. He authored or co-authored more than 250 publications, including more than 100 journal papers (66 of which published in IEEE journals) and over 200 peer-reviewed international conference papers and book chapters. His contributions have been extremely influential in many different fields, namely phase estimation and unwrapping, convex optimization and Bayesian inference for imaging inverse problems, with a special emphasis on remote sensing, including synthetic aperture radar (SAR), hyperspectral unmixing, fusion, superresolution, classification, and segmentation. In this paper, we provide an overview of his outstanding contributions to remote sensing image processing.

关键词： remote sensing pattern recognition optimization signal and image processing inverse problems

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