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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Mingjie Yang Xinrui Jiang Deqing Mao Jine Yan Yin Zhang Yulin Huang Jianyu Yang School of Information and Communication Engineering University of Electronic Science and Technology of China Beijing Institute of Remote Sensing Equipment Beijing China School of Optoelectronic Engineering Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In forward-looking scanning radar imaging, spectral estimation method can improve the azimuth resolution. However, when dealing with large-scale echo data, the computational complexity is significantly elevated due to the inversion of high-dimensional matrices. To solve these problems, this paper proposes a low rank approximate iterative adaptive angular (IAA) super-resolution imaging method based on deep networks. By combining the advantages of data driven and model driven, this method avoids the problems of manual parameter selection and slow iteration update in traditional algorithm. At the same time, due to the learning and fitting ability of deep networks, the proposed method can maintain good angular super-resolution performance. Simulation results demonstrate that the algorithm exhibits reduced computational complexity and improved resolution compared to conventional methods.

关键词： Adaptive systems Simulation Superresolution Imaging Transforms Radar Radar imaging Approximation algorithms Iterative methods Computational complexity

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Information Extraction and Analysis for the Night Time Light Data of Two Sides of Helan Mountain from 1992 to 2013

Information Extraction and Analysis for the Night Time Light...

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2022 Workshop on Electronics Communication Engineering

作者： Yuanmao, Zheng Menglin, Fan Yaling, Cai Yongfei, Song Yuanrong, He School of Environmental Science and Engineering Xiamen University of Technology Xiamen361024 China Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake area Changsha410007 China School of Film Television and Communication Xiamen University of Technology Xiamen361024 China Ningxia Survey and Monitor Institute of Land and Resources Yinchuan640106 China

ISBN: (纸本)9781510666634

Scientific estimation and dynamic monitoring of regional nighttime light evolution in long time series is an important source of information for assessing regional human nighttime economic and social activities. Based on the nighttime light remote sensing data of the long time series from 1992 to 2013, this paper took the two sides of Helan Mountain as the research object, extracted the total amount of regional light by using TNLI index, and analyzed the temporal variation characteristics of nighttime light on both sides of Helan Mountain from time and space by studying the nighttime light changes. The results show that the nighttime light of Helan Mountain and its two sides increased year by year from 1992 to 2013, and increased sharply in 1997, while the variation of total nighttime light and nighttime light density tended to be gentle from 2002 to 2013. In the temporal and spatial evolution of nighttime light in Inner Mongolia and Ningxia, the total amount of nighttime light on both sides of Helan Mountain has been increasing, but the total amount of light has changed little since 2002. The above indicated that in order to protect the ecology of Helan Mountain, the regional development on both sides of Helan Mountain changed from rapid expansion to stable upward. The research results can provide data support for the ecological protection of Helan Mountain and the formulation of policies. © 2023 SPIE.

关键词： Ecology

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A Ceph-based storage strategy for big gridded remote sensing data

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Big Earth Data 2022年第3期6卷 323-339页

作者： Xinyu Tang Xiaochuang Yao Diyou Liu Long Zhao Li Li Dehai Zhu Guoqing Li College of Land Science and Technology China Agricultural UniversityBeijingChina Key Laboratory of Remote Sensing for Agri-Hazards Ministry of Agriculture and Rural AffairsBeijingChina Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina

When using distributed storage systems to store gridded remote sensing data in large,distributed clusters,most solutions utilize big table index storage ***,in practice,the performance of big table index storage strategies degrades as scenarios become more complex,and the reasons for this phenomenon are analyzed in this *** improve the read and write performance of distributed gridded data storage,this paper proposes a storage strategy based on Ceph *** strategy encapsulates remote sensing images in the form of objects through a metadata management strategy to achieve the spatiotemporal retrieval of gridded data,finding the cluster location of gridded data through hash-like *** method can effectively achieve spatial operation support in the clustered database and at the same time enable fast random read and write of the gridded *** write and spatial query experiments proved the feasibility,effectiveness,and stability of this *** experiments prove that the method has higher stability than,and that the average query time is 38%lower than that for,the large table index storage strategy,which greatly improves the storage and query efficiency of gridded images.

关键词： Gridded images distributed storage system metadata management Ceph remote sensing

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Habitat quality evaluation and pattern simulation of coastal salt marsh wetlands

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science of the Total Environment 2024年 945卷 174003页

作者： Huang, Yuting Zheng, Guanghui Li, Xianglan Xiao, Jingfeng Xu, Zhe Tian, Pengpeng State Key Laboratory of Remote Sensing Science College of Global Change and Earth System Science Faculty of Geographical Science Beijing Normal University Beijing China School of Geographical Sciences Nanjing University of Information Science & Technology Nanjing China Earth Systems Research Center Institute for the Study of Earth Oceans and Space University of New Hampshire DurhamNH United States

Coastal salt marsh wetlands not only sequester a large amount of organic carbon, mitigating the effect of climate change, but also nurture rich wetland resources and diverse ecological environments. In this study, habitat pattern and quality of the Jiangsu Yancheng Wetland Rare Birds National Nature Reserve were studied. The evolution of habitat patterns was analyzed using the U-Net model and Sentinel-2 data. The habitat quality was evaluated using the InVEST model, while the future habitat pattern in 2027 under different scenarios were simulated using the PLUS model. Our results showed that, during 2017–2022, the Suaeda salsa habitat showed a net decrease in area of 2077.61 ha, while Spartina alterniflora and Phragmites australis habitats manifested a net increase in different degrees. The overall habitat pattern was characterized by fragmentation decline and regularization enhancement. The habitat quality decreased from 0.75 to 0.72, mainly due to the loss of the S. salsa habitat and the expansion of the P. australis habitat. The simulation results indicated that, the habitat quality is expected to further decline to 0.71 under the natural development scenario, and 390.27 ha of S. salsa habitat will convert to P. australis. While in government control scenario, the habitat quality is expected to improve to 0.78, which was 0.07 higher than that in natural development scenario, and S. salsa habitat can be restored well. This study provides a scientific basis for the protection of suitable habitats for waterfowl and is crucial for the ecological conservation and management planning of nature reserves and coastal salt marsh wetlands. © 2024 Elsevier B.V.

关键词： Wetlands

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Comparison of the Fractional Snow Cover Retrieval Capabilities of China's New Generation Geostationary Meteorological Satellites, FY-4A and FY-4B

Comparison of the Fractional Snow Cover Retrieval Capabiliti...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Fangbo Pan Lingmei Jiang Gongxue Wang State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences Faculty of Geographical Science Beijing Normal University Beijing China College of Geomatics Xi’an University of Science and Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

One of the main features of the Asian Water Tower imbalance is the massive melting of snow, necessitating enhanced snow monitoring. However, the sensors aboard polar-orbiting satellites, such as MODIS, yield only one to two valid observations daily. This, coupled with the extensive cloud cover and prolonged duration over the Asian Water Tower, results in a significant number of data gaps. China's new generation of geostationary satellites FY-4A and FY-4B have high frequency observations, making it possible to monitor snow with high precision. In this study, we systematically analyze the image pixel size stretching of FY-4A and FY-4B in the Asian Water Tower region, based on their imaging geometry. This analysis offers a theoretical foundation for fractional snow cover retrieval in the subsequent integration of these two satellites. Concurrently, this study conducts fractional snow cover (FSC) retrieval for FY-4A and FY-4B, utilizing the multiple endmember spectral mixture analysis algorithm with automatic endmember extraction (MESMA-AGE). High spatial resolution Landsat-8 imagery serves as reference data for accuracy assessment. The results indicated that FY-4A's retrieval accuracy remained unaffected by pixel size stretching, achieving an Overall Accuracy (OA) of up to 0.97 and a Root Mean Square Error (RMSE) of less than 0.13. For FY-4B, the retrieval accuracy demonstrated higher snow identification precision, with an OA of up to 0.95. However, the RMSE varied significantly due to pixel size stretching, ranging from 0.12 to 0.21. FY-4A and FY-4B fusion enables high precision and near-real-time snow monitoring.

关键词： Geometry Earth Accuracy Snow Poles and towers Imaging Distance measurement

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Individual tree extraction from terrestrial laser scanning data via graph pathing

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Forest Ecosystems 2021年第4期8卷 903-913页

作者： Di Wang Xinlian Liang Gislain II Mofack Olivier Martin-Ducup Department of Remote Sensing Science and Technology School of Electronic EngineeringXidian University710077 Xi’anChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University430079 WuhanChina Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute02430 MasalaFinland Plant Systematics and Ecology Laboratory Higher Teacher’s Training CollegeUniversity of Yaounde IBP 337 YaoundeCameroon AMAP Univ MontpellierIRDCNRSCIRADINRAEF-34000 MontpellierFrance

Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical *** task currently is undertaken through laborious and time-consuming manual assistance and quality *** study presents a new fully automatic approach to extract single trees from large-area TLS *** data-driven method operates exclusively on a point cloud graph by path finding,which makes our method computationally efficient and universally applicable to data from various forest ***:We demonstrated the proposed method on two openly available ***,we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest ***,we successfully extracted 270 trees from one hectare temperate *** validation resulted in a mean Intersection over Union(mIoU)of 0.82 for single crown segmentation,which further led to a relative root mean square error(RMSE%)of 21.2% and 23.5% for crown area and tree volume estimations,***:Our method allows automated access to individual tree level information from TLS point *** proposed method is free from restricted assumptions of forest *** is also computationally efficient with an average processing time of several seconds for one million *** is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications,ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.

关键词： Point cloud Segmentation Tree extraction Graph pathing

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Modeling sea ice in the marginal ice zone as a dense granular flow with rheology inferred from discrete element model data

arXiv

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arXiv 2024年

作者： de Diego, Gonzalo G. Gupta, Mukund Gering, Skylar A. Harris, Rohaiz Stadler, Georg Courant Institute New York University New York United States Department of Geoscience and Remote Sensing Delft Institute of Technology Netherlands Environmental Science and Engineering California Institute of Technology CA United States

The marginal ice zone (MIZ) represents the periphery of the sea ice cover. In this region, the macroscale behavior of the sea ice results from collisions and enduring contact between ice floes. This configuration closely resembles that of dense granular flows, which have been modeled successfully with the μ(I) rheology. Here, we present a continuum model based on the μ(I) rheology which treats sea ice as a compressible fluid, with the local sea ice concentration given by a dilatancy function Φ(I). We infer expressions for μ(I) and Φ(I) by nonlinear regression using data produced with a discrete element method (DEM) which considers polygonal-shaped ice floes. We do this by driving the sea ice with a one-dimensional shearing ocean current. The resulting continuum model is a nonlinear system of equations with the sea ice velocity, local concentration, and pressure as unknowns. The rheology is given by the sum of a plastic and a viscous term. In the context of a periodic patch of ocean, which is effectively a one dimensional problem, and under steady conditions, we prove this system to be well-posed, present a numerical algorithm for solving it, and compare its solutions to those of the DEM. These comparisons demonstrate the continuum model's ability to capture most of the DEM's results accurately. The continuum model is particularly accurate for ocean currents faster than 0.25 m/s;however, for low concentrations and slow ocean currents, the continuum model is less effective in capturing the DEM results. In the latter case, the lack of accuracy of the continuum model is found to be accompanied by the breakdown of a balance between the average shear stress and the integrated ocean drag extracted from the DEM. Since this balance is expected to hold independently of our choice of rheology, this finding indicates that continuum models might not be able to describe sea ice dynamics for low concentrations and slow ocean currents. © 2024, CC BY.

关键词： Continuum mechanics

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Research on warning threshold of horizontal displacement of landslide monitoring based on clustering idea

Research on warning threshold of horizontal displacement of ...

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2024 International Conference on remote sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： He, Xiaolong Dong, Yi Gao, Xuhe Li, Fei China Coal Aerial Survey and Remote Sensing Group Co. LTD Shaanxi Xi'an710199 China State Key Laboratory of Mountain Bridge and Tunnel Engineering School of Civil Engineering Chongqing Jiaotong University Chongqing400074 China Key Laboratory of Mine Disaster Prevention and Control North China Institute of Science and Technology Langfang065201 China

ISBN: (纸本)9781510685826

Unreasonable threshold setting is an important factor leading to high false alarm rate of landslide monitoring and warning. In order to obtain accurate threshold warning intervals for most landslide warning levels, we taken GNSS horizontal displacement parameters as an example, selected 60 successful landslide warning cases in Ankang City, Shaanxi Province, and extracted the corresponding GNSS horizontal displacement changes. After data filtered, applied parameter standard normalization and K-means clustering algorithm to obtain four centroid positions adaptively, thus divided five landslide monitoring threshold warning level intervals. Used this threshold level standard, we judged the threshold warning level of the remaining 5 successful landslide warning cases and obtained good warning results. The research showed that the K-means clustering method can obtain the monitoring and warning threshold level suitable for most landslide hazard points, which was conducive to reduce the false alarm rate of landslide disaster threshold warning. © 2024 SPIE.

关键词： Landslides

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A Recalibration Method for Historical SAR Data Through Inter-Satellite Alignment

A Recalibration Method for Historical SAR Data Through Inter...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Yu Xiao Yongsheng Zhou Lingling Ma Fei Ma Qiang Yin Fan Zhang College of Information Science and Technology Beijing University of Chemical Technology Beijing China National Engineering Laboratory for Satellite Remote Sensing Applications Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recalibration of historical Synthetic Aperture Radar (SAR) data facilitates the adjustment of data that have not undergone radiometric calibration or have suboptimal calibration accuracy, ensuring consistency among diverse historical images. This consistency is crucial for long-term sequential remote sensing quantitative applications. Traditional calibration methods based on artificial calibrators require the presence of such calibrators in historical data, a condition often unfulfilled, rendering the recalibration process impractical. In contrast, cross-calibration methods based on natural scenes only necessitate contemporaneous and co-located calibrated satellite data to recalibrate historical datasets. However, existing cross-calibration methods for historical data recalibration typically utilize data from a single SAR sensor. This approach inadequately corrects system errors within the SAR sensor, resulting in low calibration accuracy. To address this issue, this paper proposes a radiometric recalibration method for historical SAR data based on inter-satellite data alignment. Initially, suitable data from different SAR satellite sensors operating in the same frequency band are selected. Subsequently, a resampling method is employed to construct cross-calibration data pairs with consistent resolution. Finally, the recalibration coefficients are fitted using a Gaussian Mixture Model (GMM) to mitigate the impact of anomalous interference values. Experiment results on Sentinel-1A and GF-3A data demonstrated the effectiveness of the proposed method. Compared to traditional fitting methods, the proposed method reduced calibration errors in saline-alkali and bare land areas by 0.82 dB and 0.77 dB, respectively.

关键词： Accuracy Satellite broadcasting Mixture models Interference Radiometry Rendering (computer graphics) Robustness Calibration Synthetic aperture radar remote sensing

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A precise spatiotemporal fusion crop classification framework based on parcels

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Scientific Reports 2025年第1期15卷 1-24页

作者： Xia, Liegang Chen, Changge Luo, Jiancheng Hu, Xiaodong Lu, Xuanzhi Yu, Hongfeng Lu, Keyu College of Computer Science and Technology Zhejiang University of Technology Zhejiang 310023 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100101 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou 310023 China

The precise extraction of crop type information on agricultural land supports applications such as agricultural information statistics and planning. It is also a crucial foundation for improving agricultural production efficiency and promoting agricultural informatization. In smallholder agricultural regions, such as the southern agricultural areas of China, a significant number of small parcels exist. These small parcels often exhibit deficiencies and discrepancies in feature representation for time series classification of crop types, leading to considerable classification challenges. To achieve more precise crop type differentiation in smallholder agricultural systems, this study designs a parcel-based classification framework, PITT (Parcel-level Integration of Time series and Texture). The PITT framework categorizes small parcels in smallholder systems by area into small parcels and micro parcels, which are then separately used as inputs for time series classification methods and high-resolution texture classification methods. During the process, the time series classification results guide the high-resolution texture classification method. Finally, the results from the texture classification are fused with the time series classification results, achieving more accurate crop classification outcomes. The study focuses on the Jiang area of Zongyang County, Tongling city, Anhui Province. Experimental validations using Pearson correlation coefficients and TWDTW similarity comparisons reveal that larger parcels have time series features that more strongly represent the features of typical samples. Additionally, when the PITT framework was compared with other time series classification models using real labels, the F1 scores for small parcels of approximately 0.1–0.5 hectares increased for rapeseed and wheat, reaching 0.93 and 0.94, respectively. For micro parcels (less than 0.1 ha), the F1 scores improved by at least 4.11% and 17.05%, respectively. This demonstrates

关键词： Agricultural phenology Crop classification Object-based texture classification Synthetic aperture radar (SAR) Time series

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