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Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Acta Oceanologica Sinica 2022年第7期41卷 180-192页

作者： Yongxu Li Xudong Lai Mingwei Wang School of Remote Sensing and Information Engineering Wuhan UniversityWuhan430079China School of Computer Science Hubei University of TechnologyWuhan430068China

Ship detection using synthetic aperture radar(SAR)plays an important role in marine *** existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongly detected in complex *** non-ship false alarms can be excluded by training discriminators,and the desired accuracy is obtained with enough verified ***,the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation,which is difficult and time *** address this issue,a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate ***,various features commonly used in SAR image target discrimination are extracted,and several acknowledged classification models and their classic variants are *** discriminators are constructed by dividing these features into different groups and pairing them with each ***,the performance of all the discriminators is tested,and better discriminators are selected for implementing the semisupervised training *** strategies enhance the diversity and reliability of the discriminators,and their heterogeneous ensemble makes more correct judgments on candidate targets,which facilitates further positive *** results demonstrate that the proposed method outperforms traditional tritraining.

关键词： synthetic aperture radar ship target discrimination non-ship false alarms semisupervised heterogeneous ensemble

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Reconstruction of Spatially Seamless Soil Moisture Data from SMAP Product Using Deep Learning

Reconstruction of Spatially Seamless Soil Moisture Data from...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Fan, H.Y. Li, Z.S. Jin, Y. Jia, Y. Jin, S.G. Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering China

ISBN: (纸本)9798350375909

This paper proposes a deep learning method for spatial data imputation, utilizing a CNN with residual connections to construct a regression model for missing value prediction. The introduction of temporal features from SMAP ensures seamless spatial characteristics for soil moisture. By integrating various spatially seamless, multi-source remote sensing data as auxiliary variables, the method predicts and fills the spatial gaps in SMAP SSM. The result is a long-term, spatially seamless daily soil moisture dataset with a 9km resolution, covering the period from January 1, 2016, to December 31, 2022. The effectiveness of this spatial imputation method is validated by comparing and analyzing it with site-measured data and original SMAP data. This approach provides a solution for generating spatially seamless soil moisture data. © 2024 IEEE.

关键词： Soil moisture

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Dust Source Emission Forecasting using GraphSAGE and Delaunay Graph 10

Dust Source Emission Forecasting using GraphSAGE and Delauna...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Sanisales, Maryam Vefghi, Ali Rahmati, Zahed Boloorani, Ali Darvishi Amirkabir University of Technology Department of Mathematics and Computer Science Tehran Iran University of Tehran Faculty of Geography Department of Remote Sensing and GIS Tehran Iran

ISBN: (纸本)9798331532543

Our work proposes a method for forecasting dust source emission (DSE) using a graph neural network (GNN). Dust storms pose a significant environmental and health hazard, and accurately forecasting their origin is crucial for mitigation strategies. Traditional methods like direct field measurements are limited in coverage or resolution. This work introduces a framework that constructs a novel connected graph, Delaunay graph from historical dust hotspot events, incorporating spatial and temporal relationships between environmental parameters obtained from remote sensing data. A state-of-the-art GNN model, inductive GraphSAGE, is applied to this graph to predict future active dust source locations. The proposed method outperforms a baseline random forest model, demonstrating its ability to capture temporal relationships throughout months. The AUC of GraphSAGE for dust source emission forecasting was approximately 73% which is 17% better than a simple random forest. © 2024 IEEE.

关键词： dust source emission forcasting graph neural network remote sensing Styling GraphSAGE

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Diverse Image Captioning via Sequential Variational Transformer and Muti-feature Fusion 24

Diverse Image Captioning via Sequential Variational Transfor...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Chen, Siyu Xiang, Bo Liu, Junrong Aerial Survey and Remote Sensing Bureau of China Coal Geology Administration Xi'an China School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou China

ISBN: (纸本)9798400717192

The transformer-based image captioning models have shown remarkable performance based on the powerful sequence modeling capability. However, most of them focus only on learning deterministic mappings from image space to caption space, i.e., learning how to improve the accuracy of predicting average"captions, which generally leads to common words, repeated phrases and single sentence. In this paper, we propose a novel multi-feature fusion based sequential variational transformer for diverse image captioning (MF-SVT-DIC), aiming to learn one-to-many projections and improve the diversity and accuracy of generated captions simultaneously. Specifically, we incorporate sequential variational inference into the traditional transformer-based captioning model to model the word-level diversity. Meanwhile, we design a fusion module to take advantage of both grid features and region features, facilitating the generation of fine-grained captions. The experimental results demonstrate that our method achieves significant gains in term of both diversity and accuracy compared with the state-of-the-art diverse image captioning models. © 2024 ACM.

关键词： Image fusion

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An Overview of Optical Schemes for Spaceborne Greenhouse Gas Passive remote sensing Payload

An Overview of Optical Schemes for Spaceborne Greenhouse Gas...

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2024 Applied Optics and Photonics China: Atmospheric and Environmental Optics, AOPC 2024

作者： Wu, Qiong Luo, Hai Yan Li, Zhi Wei Lin, Han Jin, Wei Ding, Yi Xiong, Wei University of Science and Technology of China Hefei230026 China Anhui Province Key Laboratory of Optical Quantitative Remote Sensing Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China

ISBN: (数字)9781510687981

ISBN: (纸本)9781510687974

Anthropogenic emissions of greenhouse gases (GHGs), particularly carbon dioxide (CO2) and methane (CH4), constitute the primary drivers of global warming. Controlling anthropogenic emissions is crucial in mitigating global warming. Satellite remote sensing technology is considered the most viable and effective technological support for carbon monitoring. Global-scale, long-term carbon monitoring enhances understanding of human activities' impact on carbon cycles and climate change, while high spatiotemporal resolution carbon monitoring in key regions aims to provide data support in reducing anthropogenic emissions. Passive optical remote sensing is considered the primary technological means for satellite-based carbon monitoring. The satellite-borne passive remote sensing detection technologies successfully validated in orbit include Michelson interferometric spectroscopy, grating spectroscopy, Fabry-Pérot technology, and spatial heterodyne interferometric spectroscopy. This article reviews recent advancements in optical solutions for remote sensing payloads. It thoroughly analyzes the optical performance metrics of these payloads, comparing the strengths and weaknesses of different detection technologies through optical scheme analyses. Furthermore, specific metrics and development trends for passive payloads used in high spatiotemporal resolution remote sensing of key areas have been discussed. Finally, considering the technical requirements for China's next-generation carbon satellite. A novel static interferometric imaging technique is proposed, which combines spatial heterodyne interferometric spectral technology with azimuthal arc vector orthogonal direction heterogeneous optical field modulation. This innovative technology retains the advantages of traditional spatial heterodyne interferometry with high optical throughput and spectral resolution, while introducing new modulation techniques for enhanced spatial resolution. It is anticipated to advance global en

关键词： Fabry-Perot interferometers

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Research on forest land classification method of remote sensing images based on boundary information 4

Research on forest land classification method of remote sens...

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Yuan, Zhengwu Wang, Yang Yang, Aixia Chen, Qiang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Semantic segmentation of remote sensing image is a key technology in the field of remote sensing image processing, and its segmentation results can be used in land resource management, yield estimation, disaster evaluation and many other aspects. However, the forest land is widely distributed and the tree species are diverse, which brings difficulties to the extraction of forest land. Different from traditional manual investigation, semantic segmentation can quickly extract forest land from remote sensing images. U-Net is a deep codec structure, which has been frequently used for high-precision image segmentation. In this paper, Multi-scale features of different levels of U-Net are used to extract forest land from high-resolution remote sensing images. Multi-scale features can capture features of different scales for fusion, and note the importance of boundary information. A boundary attention module is added to explicitly use boundary information for context aggregation, which makes the extracted boundary effect more remarkable Attention module is designed to enhance the learning of vegetation characteristics, so as to improve the segmentation performance. This study effectively improves the problems of fuzzy boundary of semantic segmentation, large intra-class differences and small inter-class differences, and can quickly extract forest land. © 2024 SPIE.

关键词： Extraction

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Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms

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Space(science & technology) 2024年第1期4卷 663-673页

作者： Liqiang Zhang Qingxia Li Haitao Qiu Qingjun Zhang Yixin Gao Rong Jin Rui Wang Huan Zhang Zhongkai Wen Jian Zhang School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China Insititute of Remote Sensing Satellite China Academy of Space TechnologyBeijing 100094China Aerospace Long-march International Trade Co. Ltd.Beijing 100054China

In order to alleviate the impact of radio frequency interference(RFI)on the accuracy of ocean salinity satellite remote sensing,scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS ***,the signals that generate RFI are diverse,and the factors that influence remote sensing observation data are *** algorithms often target specific hypothetical conditions,lacking general applicability,which frequently leads to an important gap between the nominal performance of the literature and practical applications,posing great challenges to data labeling *** address this problem,this study conducted a comprehensive and systematic analysis of RFI simulation based on scene modeling,algorithm modeling,and RFI energy *** typical RFI detection algorithms were selected,and the simulation scene was divided into 3 typical scenes:ocean,land,and sea–land scenes,and RFI was analyzed in terms of weak,moderate,strong,and extremely strong based on *** simulation analysis and evaluation of RFI detection algorithms,lookup tables for algorithm selection,detection rate,and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above *** lookup tables have universal guiding significance and provide reliability assurance in complex situations.

关键词： ocean salinity satellite remote sensingscholars radio frequency interference rfi satellite rfi detection remote sensing detection labeling algorithms remote sensing observation remote sensing data smos satellitehoweverthe

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Low frequency error analysis and calibration for multiple star sensors system of GaoFen7 satellite

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地球空间信息科学学报（英文版） 2024年第1期27卷 82-94页

作者： Yanli Wang Mi Wang Ying Zhu Xiaoxiang Long The College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdaoChina The State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanChina School of Electrical and Information Engineering Wuhan Institute of TechnologyWuhanChina Value-added Service Department China Center for Resources Satellite Data and ApplicationBeijingChina

The GaoFen7(GF7)optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic *** improve the accuracy of attitude determination,an attitude determination system comprised of four star sensors is *** to the measurement accuracy and steady performance,the star sensors 1a and 1b is usually used together for satellite attitude calculation,which is called the conventional mode of attitude ***,the combination of star sensors 2a and 2b is called the unconventional mode of attitude *** by variations in the incident angle of sunlight and solar radiation,thermal deformation occurs in the body and installation structure of the star sensor,which causes Attitude Low-Frequency Error(ALFE)and seriously influences the con-sistency of attitude determination results of different combination modes for multiple star sensors *** study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination *** on the statistical characteristics of the angles of the three axes,the installation parameters of the four star sensors are first *** analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days,the segmented ALFE compensation model between the unconventional and conven-tional modes is proposed based on the Fourier series model and input parameter of *** on the on-orbit installation parameters and the ALFE model,the precise attitude determination results of the unconventional mode are *** results show that the attitude determination consistency after compensation is better than 2".Moreover,the reliable application time range of the compensation model is 30 days to satisfy the re

关键词： Attitude Low Frequency Error(ALFE) multiple star sensors system GaoFen7 attitude determination consistency Fourier series

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Automatic Feature Extraction from Satellite Imagery for remote sensing 7

Automatic Feature Extraction from Satellite Imagery for Remo...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Mary, D Diya Nair, Nandana S. Mohana Revathy, S. Gladence, L. Mary Bernatin, T. Sathyabama Institute of Science and Technology Department of Information Technology Chennai India Centre for Remote Sensing and Geoinformatics Sathyabama Institute of Science and Technology Chennai India School of Computing Sathyabama Institute of Science and Technology Chennai India Sathyabama Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350397284

Feature extraction from satellite images has steadily but progressively grown in recent years. Numerous feature extraction techniques have been created as a result of the increase. The loss of certain features is one of the various obstacles that must be overcome while selecting the best path for feature extraction. This study suggests utilizing a Convolutional Neural Network (CNN) approach to tackle these issues and extract attributes from satellite images supplied by an open source dataset called MLRSNet. Images with labels explaining each image's characteristics are displayed in the output. This makes it simpler to recognize and understand different components in satellite images. © 2023 IEEE.

关键词： Feature extraction

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Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP)

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Arabian Journal of Geosciences 2024年第3期17卷 1-16页

作者： Gautam, Pawan Kumar Chandra, Sushil Henry, Pradnita Komal Remote Sensing Applications Centre Uttar Pradesh Department of Science & Technology Lucknow India

Severe flood events during the last decades have proved to be a devastating disaster for the densely populated and economy of Uttar Pradesh. Due to its ability to provide a concise view of spatial extent of the flood, remote sensing techniques and synthetic aperture radar (SAR) systems in particular have been applied to flood mapping. The present study has identified flood-prone areas in Uttar Pradesh using the C-band (SAR) sensor of Sentinel-1. In this study, changes in flood inundation area during the monsoon season of 2021 have been estimated. Sentinel Application Platform (SNAP) has been used to perform SAR pre-processing which includes orbit file application, thermal noise removal, calibration, speckle filtering, and terrain correction. Subsequently, polarization, band math expression, thresholding, and image are processed. In addition, image mapping has been done in Arc-GIS tool for detailed investigation of flood inundation area. Detailed investigation of the flood inundated area found that overall, 7710 km2 is affected by the flood area. This study will be the potential use of microwave SAR remote sensing as a comprehensive and effective way of providing accurate surface water information for water resources management, flood warning, flood monitoring, and rapid and accurate flood damage assessment in the years to come.

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