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Journal articles on the topic 'Multi-accuracy spatial data'

Author: Grafiati

Published: 11 March 2023

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1

Belussi, Alberto, and Sara Migliorini. "A framework for integrating multi-accuracy spatial data in geographical applications." GeoInformatica 16, no. 3 (2011): 523–61. http://dx.doi.org/10.1007/s10707-011-0140-9.

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Jeong, Weonil. "Multi-level Load Shedding Scheme to Increase Spatial Data Stream Query Accuracy." Journal of the Korea Academia-Industrial cooperation Society 16, no. 12 (2015): 8370–77. http://dx.doi.org/10.5762/kais.2015.16.12.8370.

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Järv, Olle, Henrikki Tenkanen, and Tuuli Toivonen. "Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation." International Journal of Geographical Information Science 31, no. 8 (2017): 1630–51. http://dx.doi.org/10.1080/13658816.2017.1287369.

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Shimizu, Katsuto, Tetsuji Ota, Nobuya Mizoue, and Hideki Saito. "Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests." Remote Sensing 12, no. 11 (2020): 1876. http://dx.doi.org/10.3390/rs12111876.

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Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using

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Tu, Jinsheng, Haohan Wei, Rui Zhang, et al. "GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data." Remote Sensing 13, no. 21 (2021): 4311. http://dx.doi.org/10.3390/rs13214311.

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Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed,

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Rignot, Eric, and Mark R. Drinkwater. "Winter Sea-ice mapping from multi-parameter synthetic-aperture radar data." Journal of Glaciology 40, no. 134 (1994): 31–45. http://dx.doi.org/10.1017/s0022143000003774.

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AbstractThe limitations of current and immediate future single-frequency, single-polarization, space-borne SARs for winter sea-ice mapping are quantitatively examined, and improvements are suggested by combining frequencies and polarizations. Ice-type maps are generated using multi-channel, air-borne SAR observations of winter sea ice in the Beaufort Sea to identify six ice conditions: (1) multi-year sea ice; (2) compressed first-year ice; (3) first-year rubble and ridges; (4) first-year rough ice; (5) first-year smooth ice; and (6) first-year thin ice. At a single polarization, C- (λ = 5.6 cm

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Rignot, Eric, and Mark R. Drinkwater. "Winter Sea-ice mapping from multi-parameter synthetic-aperture radar data." Journal of Glaciology 40, no. 134 (1994): 31–45. http://dx.doi.org/10.3189/s0022143000003774.

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AbstractThe limitations of current and immediate future single-frequency, single-polarization, space-borne SARs for winter sea-ice mapping are quantitatively examined, and improvements are suggested by combining frequencies and polarizations. Ice-type maps are generated using multi-channel, air-borne SAR observations of winter sea ice in the Beaufort Sea to identify six ice conditions: (1) multi-year sea ice; (2) compressed first-year ice; (3) first-year rubble and ridges; (4) first-year rough ice; (5) first-year smooth ice; and (6) first-year thin ice. At a single polarization, C- (λ = 5.6 cm

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Yao, Zhiying, Yuanyuan Zhao, Hengbin Wang, et al. "Comparison and Assessment of Data Sources with Different Spatial and Temporal Resolution for Efficiency Orchard Mapping: Case Studies in Five Grape-Growing Regions." Remote Sensing 15, no. 3 (2023): 655. http://dx.doi.org/10.3390/rs15030655.

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As one of the most important agricultural production types in the world, orchards have high economic, ecological, and cultural value, so the accurate and timely mapping of orchards is highly demanded for many applications. Selecting a remote-sensing (RS) data source is a critical step in efficient orchard mapping, and it is hard to have a RS image with both rich temporal and spatial information. A trade-off between spatial and temporal resolution must be made. Taking grape-growing regions as an example, we tested imagery at different spatial and temporal resolutions as classification inputs (i

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Carl, Gudrun, Sam Levin, and Ingolf Kühn. "spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data." Biodiversity Data Journal 6 (February 28, 2018): e20760. http://dx.doi.org/10.3897/bdj.6.e20760.

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spind is an R package aiming to provide a useful toolkit to account for spatial dependence in the analysis of lattice data. Grid-based data sets in spatial modelling often exhibit spatial dependence, i.e. values sampled at nearby locations are more similar than those sampled further apart. spind methods, described here, take this kind of two-dimensional dependence into account and are sensitive to its variation across different spatial scales. Methods presented to account for spatial autocorrelation are based on the two fundamentally different approaches of generalised estimating equations as

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Kozoderov, V. V., and V. D. Egorov. "Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation." Исследования Земли из Космоса, no. 6 (December 21, 2019): 89–102. http://dx.doi.org/10.31857/s0205-96142019689-102.

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Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were d

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Lu, Zhi Gang, Jian Ya Gong, and Xing Quan Liu. "Prediction Analysis of Pit Deformation Based on Spatial Data Mining." Applied Mechanics and Materials 178-181 (May 2012): 2357–64. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.2357.

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Pit excavation was easy to cause the deformation of the supporting structure and surrounding soil, and brought serious harm to the surrounding buildings and urban underground pipelines. How to carry on a comprehensive analysis of inter-linked pit monitoring points, and improve the overall prediction accuracy was the urgent problem needed to be solved in scientific predictions of pit deformation. In order to establish the multi-variable gray theory GM(1,N) first-order linear dynamic model, using pit mutual influential settlement deformation monitoring data, and the correlation degree analysis,

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Yan, Jiahe, Honghui Li, Yanhui Bai, and Yingli Lin. "Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition." Applied Sciences 11, no. 19 (2021): 9220. http://dx.doi.org/10.3390/app11199220.

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As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering “temporal-spatial-periodic” characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition

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Gong, Yali, Huan Xie, Yanmin Jin, and Xiaohua Tong. "Assessing Multi-Temporal Global Urban Land-Cover Products Using Spatio-Temporal Stratified Sampling." ISPRS International Journal of Geo-Information 11, no. 8 (2022): 451. http://dx.doi.org/10.3390/ijgi11080451.

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In recent years, the availability of multi-temporal global land-cover datasets has meant that they have become a key data source for evaluating land cover in many applications. Due to the high data volume of the multi-temporal land-cover datasets, probability sampling is an efficient method for validating multi-temporal global urban land-cover maps. However, the current accuracy assessment methods often work for a single-epoch dataset, and they are not suitable for multi-temporal data products. Limitations such as repeated sampling and inappropriate sample allocation can lead to inaccurate eva

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Jayababu, Y., G. P. S. Varma, and A. Govardhan. "Mining Spatial Association Rules to Automatic Grouping of Spatial Data Objects Using Multiple Kernel-Based Probabilistic Clustering." Journal of Intelligent Systems 26, no. 3 (2017): 561–72. http://dx.doi.org/10.1515/jisys-2016-0044.

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AbstractWith the extensive application of spatial databases to various fields ranging from remote sensing to geographical information systems, computer cartography, environmental assessment, and planning, discovery of interesting and hidden knowledge in the spatial databases is a considerable chore for classifying and using the spatial data and knowledge bases. The literature presents different spatial data mining methods to mine knowledge from spatial databases. In this paper, spatial association rules are mined to automatic grouping of spatial data objects using a candidate generation proces

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Kumar, U., C. Milesi, R. R. Nemani, and S. Basu. "Multi-sensor multi-resolution image fusion for improved vegetation and urban area classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 51–58. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-51-2015.

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In this paper, we perform multi-sensor multi-resolution data fusion of Landsat-5 TM bands (at 30 m spatial resolution) and multispectral bands of World View-2 (WV-2 at 2 m spatial resolution) through linear spectral unmixing model. The advantages of fusing Landsat and WV-2 data are two fold: first, spatial resolution of the Landsat bands increases to WV-2 resolution. Second, integration of data from two sensors allows two additional SWIR bands from Landsat data to the fused product which have advantages such as improved atmospheric transparency and material identification, for example, urban f

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Cole, B., J. L. Awange, and A. Saleem. "Environmental spatial data within dense tree cover: exploiting multi-frequency GNSS signals to improve positional accuracy." International Journal of Environmental Science and Technology 17, no. 5 (2020): 2697–706. http://dx.doi.org/10.1007/s13762-020-02634-y.

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Nepoklonov, Vicktor, Mayya Maximova, and Ivan Sukharev-Krylov. "Monitoring of spatial data coordinate basis integrity using coordinate transformations." E3S Web of Conferences 310 (2021): 03009. http://dx.doi.org/10.1051/e3sconf/202131003009.

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The modern spatial data coordinate basis (SDCB) is built taking into account the variety of existing and used today geodetic networks, models of physical fields of the Earth, cartographic models, as well as coordinate systems (СS). One of the requirements for SDCB from the standpoint of system analysis is the requirement of integrity, which presupposes the unity of the determination of coordinates, that is, the consistency of the results of determining the coordinates of the same points in different CSs. The article is devoted to the monitoring of the accuracy characteristics of the available

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Zhang, Jia, Xiulian Wang, Xiaotong Zhang, Xiaofei Bai, and Qiang Chen. "Construction of multi-scale grid for massive land survey data." E3S Web of Conferences 206 (2020): 03018. http://dx.doi.org/10.1051/e3sconf/202020603018.

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In the face of ever-growing and complex massive multi-source spatiotemporal data, the traditional vector data model is increasingly difficult to meet the needs of efficient data organization, management, calculation and analysis. Based on the simple and widely used geographic grid data organization model, this paper designs a technical method to convert vector data into multi-scale grid data, establishes a unified, standardized and seamless land spatial grid data model, and analyses the area accuracy of multi-scale grid data. Practice shows that the model can better meet the needs of multi-sca

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Chen, Zhenting, Junfeng Wang, Dongyang Gao, Bing Xu, Wenjie Yu, and Min Yang. "Dynamic Spatial Fusion of Cloud Vertical Phase from CALIPSO and CloudSat Satellite Data." Photogrammetric Engineering & Remote Sensing 87, no. 1 (2021): 61–67. http://dx.doi.org/10.14358/pers.87.1.61.

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Cloud phase is a core parameter of inversion of cloud characteristics. The accuracy of cloud phase affects the results of cloud optical and microphysical characteristics. In this study, we obtain the cloud vertical phase (CVP ) products of CALIPSO and CloudSat satellites, then we put forward a dynamic spatial fusion algorithm for the fusion of the two products. A series of spatial optimal CVP fusion rules are presented for dual-source data, and we realize CVP fusion using these rules. We took Typhoon Lupit in the Pacific Ocean as an experimental object. The results show that the total cloud pi

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Feng, Quanlong, Jianyu Yang, Yiming Liu, et al. "Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network." Remote Sensing 12, no. 10 (2020): 1668. http://dx.doi.org/10.3390/rs12101668.

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Vegetable mapping from remote sensing imagery is important for precision agricultural activities such as automated pesticide spraying. Multi-temporal unmanned aerial vehicle (UAV) data has the merits of both very high spatial resolution and useful phenological information, which shows great potential for accurate vegetable classification, especially under complex and fragmented agricultural landscapes. In this study, an attention-based recurrent convolutional neural network (ARCNN) has been proposed for accurate vegetable mapping from multi-temporal UAV red-green-blue (RGB) imagery. The propos

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Xu, Lijuan, Xiao Ding, Dawei Zhao, Alex X. Liu, and Zhen Zhang. "A Three-Dimensional ResNet and Transformer-Based Approach to Anomaly Detection in Multivariate Temporal–Spatial Data." Entropy 25, no. 2 (2023): 180. http://dx.doi.org/10.3390/e25020180.

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Anomaly detection in multivariate time series is an important problem with applications in several domains. However, the key limitation of the approaches that have been proposed so far lies in the lack of a highly parallel model that can fuse temporal and spatial features. In this paper, we propose TDRT, a three-dimensional ResNet and transformer-based anomaly detection method. TDRT can automatically learn the multi-dimensional features of temporal–spatial data to improve the accuracy of anomaly detection. Using the TDRT method, we were able to obtain temporal–spatial correlations from multi-d

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Zhu, Xiao, Yuanyuan Wang, Sina Montazeri, and Nan Ge. "A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data." Remote Sensing 10, no. 9 (2018): 1374. http://dx.doi.org/10.3390/rs10091374.

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Since its launch in 2007, TerraSAR-X has continuously provided spaceborne synthetic aperture radar (SAR) images of our planet with unprecedented spatial resolution, geodetic, and geometric accuracy. This has brought life to the once inscrutable SAR images, which deterred many researchers. Thanks to merits like higher spatial resolution and more precise orbit control, we are now able to indicate individual buildings, even individual floors, to pinpoint targets within centimeter accuracy. As a result, multi-baseline SAR interferometric (InSAR) techniques are flourishing, from point target-based

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Pena, J. A., T. Yumin, H. Liu, B. Zhao, J. A. Garcia, and J. Pinto. "REMOTE SENSING DATA FUSION TO DETECT ILLICIT CROPS AND UNAUTHORIZED AIRSTRIPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1363–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1363-2018.

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Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic informa

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Wang, Tong, Xin Xu, Hongxia Pan, et al. "Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion." Applied Sciences 12, no. 15 (2022): 7640. http://dx.doi.org/10.3390/app12157640.

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Given the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and spatial information contained in multi-sensor data and can realize end-to-end bearing fault diagnosis. MSDWF is committed to comprehensively characterizing the state information of bearings, and the weighted operation of the multi-sensor data is to establish the interactive information to tap into

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Wang, Mingchang, Mingjie Li, Fengyan Wang, and Xue Ji. "Exploring the Optimal Feature Combination of Tree Species Classification by Fusing Multi-Feature and Multi-Temporal Sentinel-2 Data in Changbai Mountain." Forests 13, no. 7 (2022): 1058. http://dx.doi.org/10.3390/f13071058.

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Tree species classification is crucial for forest resource investigation and management. Remote sensing images can provide monitoring information on the spatial distribution of tree species and multi-feature fusion can improve the classification accuracy of tree species. However, different features will play their own unique role. Therefore, considering various related factors about the growth of tree species such as spectrum information, texture structure, vegetation phenology, and topography environment, we fused multi-feature and multi-temporal Sentinel-2 data, which combines spectral featu

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Tu, Bing, Yu Zhu, Chengle Zhou, Siyuan Chen, and Antonio Plaza. "Optimized Spatial Gradient Transfer for Hyperspectral-LiDAR Data Classification." Remote Sensing 14, no. 8 (2022): 1814. http://dx.doi.org/10.3390/rs14081814.

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The classification accuracy of ground objects is improved due to the combined use of the same scene data collected by different sensors. We propose to fuse the spatial planar distribution and spectral information of the hyperspectral images (HSIs) with the spatial 3D information of the objects captured by light detection and ranging (LiDAR). In this paper, we use the optimized spatial gradient transfer method for data fusion, which can effectively solve the strong heterogeneity of heterogeneous data fusion. The entropy rate superpixel segmentation algorithm over-segments HSI and LiDAR to extra

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Yi, Zhiwei, Li Jia, and Qiting Chen. "Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China." Remote Sensing 12, no. 24 (2020): 4052. http://dx.doi.org/10.3390/rs12244052.

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Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on cro

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Maidaneh Abdi, I., A. Le Guilcher, and A.-M. Olteanu-Raimond. "A REGRESSION MODEL OF SPATIAL ACCURACY PREDICTION FOR OPENSTREETMAP BUILDINGS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2020 (August 3, 2020): 39–47. http://dx.doi.org/10.5194/isprs-annals-v-4-2020-39-2020.

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Abstract. Data quality assessment of OpenStreetMap (OSM) data can be carried out by comparing them with a reference spatial data (e.g authoritative data). However, in case of a lack of reference data, the spatial accuracy is unknown. The aim of this work is therefore to propose a framework to infer relative spatial accuracy of OSM data by using machine learning methods. Our approach is based on the hypothesis that there is a relationship between extrinsic and intrinsic quality measures. Thus, starting from a multi-criteria data matching, the process seeks to establish a statistical relationshi

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Zhou, Qiming, and Jianfeng Li. "Geo-Spatial Analysis in Hydrology." ISPRS International Journal of Geo-Information 9, no. 7 (2020): 435. http://dx.doi.org/10.3390/ijgi9070435.

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With the increasing demand for accurate and reliable hydrological information, geo-spatial analysis plays a more and more important role in hydrological studies. The development of the geo-spatial technique advances our understanding of the complex and spatially heterogeneous hydrological systems. Meanwhile, how to efficiently and effectively process and analyze multi-source geo-spatial data has become more challenging in the fields of hydrology. In this editorial, we first review the development and application of geo-spatial analysis in three major topics in hydrological studies, namely the

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Wang, Zhenhua, Lizhi Xu, Qing Ji, Wei Song, and Lingqun Wang. "A Multi-Level Non-Uniform Spatial Sampling Method for Accuracy Assessment of Remote Sensing Image Classification Results." Applied Sciences 10, no. 16 (2020): 5568. http://dx.doi.org/10.3390/app10165568.

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Accuracy assessment of classification results has important significance for the application of remote sensing images, which can be achieved by sampling methods. However, the existing sampling methods either ignore spatial correlation or do not consider spatial heterogeneity. Here, we proposed a multi-level non-uniform spatial sampling method (MNSS) for the accuracy assessment of classification results. Taking the remote sensing image of Kobo Askov, Texas, USA, as an example, the classification result of this image was obtained by Support Vector Machine (SVM) classifier. In the proposed MNSS,

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Liu, Xin, Junhui Wu, Yiyun Man, Xibao Xu, and Jifeng Guo. "Multi-objective recognition based on deep learning." Aircraft Engineering and Aerospace Technology 92, no. 8 (2020): 1185–93. http://dx.doi.org/10.1108/aeat-03-2020-0061.

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Purpose With the continuous development of aerospace technology, space exploration missions have been increasing year by year, and higher requirements have been placed on the upper level rocket. The purpose of this paper is to improve the ability to identify and detect potential targets for upper level rocket. Design/methodology/approach Aiming at the upper-level recognition of space satellites and core components, this paper proposes a deep learning-based spatial multi-target recognition method, which can simultaneously recognize space satellites and core components. First, the implementation

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Wang, Chuan, Shijie Liu, Xiaoyan Wang, and Xiaowei Lan. "Time Synchronization and Space Registration of Roadside LiDAR and Camera." Electronics 12, no. 3 (2023): 537. http://dx.doi.org/10.3390/electronics12030537.

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The sensing system consisting of Light Detection and Ranging (LiDAR) and a camera provides complementary information about the surrounding environment. To take full advantage of multi-source data provided by different sensors, an accurate fusion of multi-source sensor information is needed. Time synchronization and space registration are the key technologies that affect the fusion accuracy of multi-source sensors. Due to the difference in data acquisition frequency and deviation in startup time between LiDAR and the camera, asynchronous data acquisition between LiDAR and camera is easy to occu

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Li, B., L. Han, and L. Li. "A SPATIOTEMPORAL FUSION NETWORK TO MULTI SOURCE HETEROGENEOUS DATA FOR LANDSLIDE RECOGNITION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-3/W1-2022 (October 27, 2022): 77–84. http://dx.doi.org/10.5194/isprs-annals-x-3-w1-2022-77-2022.

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Abstract. In recent years, the frequency of landslide disasters has been increasing year by year due to the extension of human activities to the natural environment. Fast and detailed landslide surveys are important for landslide disaster prediction and management. There are many driving factors for landslide formation, and most of the current deep learning-based landslide identification methods use optical remote sensing images in a short period or a few types of fused data for prediction. Therefore the upper limit of accuracy they can achieve is low. This paper proposes a landslide identific

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Shi, Shuo, Sifu Bi, Wei Gong, et al. "Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection." Remote Sensing 13, no. 20 (2021): 4118. http://dx.doi.org/10.3390/rs13204118.

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The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the

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Tang, Rui, Fangling Pu, Rui Yang, Zhaozhuo Xu, and Xin Xu. "Multi-Domain Fusion Graph Network for Semi-Supervised PolSAR Image Classification." Remote Sensing 15, no. 1 (2022): 160. http://dx.doi.org/10.3390/rs15010160.

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The expensive acquisition of labeled data limits the practical use of supervised learning on polarimetric synthetic aperture radar (PolSAR) image analysis. Semi-supervised learning has attracted considerable attention as it can utilize few labeled data and very many unlabeled data. The scattering response of PolSAR data is strongly spatial distribution dependent, which provides rich information about land-cover properties. In this paper, we propose a semi-supervised learning method named multi-domain fusion graph network (MDFGN) to explore the multi-domain fused features including spatial doma

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Chen, Biyan, and Zhizhao Liu. "Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013." Atmospheric Measurement Techniques 9, no. 10 (2016): 5249–63. http://dx.doi.org/10.5194/amt-9-5249-2016.

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Abstract. Acquiring accurate atmospheric water vapor spatial information remains one of the most challenging tasks in meteorology. The tomographic technique is a powerful tool for modeling atmospheric water vapor and monitoring the water vapor spatial and temporal distribution/variation information. This paper presents a study on the monitoring of water vapor variations using tomographic techniques based on multi-source water vapor data, including GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sun

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Shao, Jianli, Xin Liu, and Wenqing He. "Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification." Mathematics 9, no. 9 (2021): 936. http://dx.doi.org/10.3390/math9090936.

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Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose

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Barazzetti, L., M. Gianinetto, and M. Scaioni. "Automatic registration of multi-source medium resolution satellite data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 23–28. http://dx.doi.org/10.5194/isprsarchives-xl-7-23-2014.

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Multi-temporal and multi-source images gathered from satellite platforms are nowadays a fundamental source of information in several domains. One of the main challenges in the fusion of different data sets consists in the registration issue, i.e., the integration into the same framework of images collected with different spatial resolution and acquisition geometry. This paper presents a novel methodology to accomplish this task on the basis of a method that stands out from existing approaches. The whole data (time series) set is simultaneously co-registered with a two-dimensional multiple Leas

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Chen, Guokun, Zicheng Liu, Qingke Wen, et al. "Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows." Remote Sensing 15, no. 5 (2023): 1228. http://dx.doi.org/10.3390/rs15051228.

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The continuous transformation from biodiverse natural forests and mixed-use farms into monoculture rubber plantations may lead to a series of hazards, such as natural forest habitats fragmentation, biodiversity loss, as well as drought and water shortage. Therefore, understanding the spatial distribution of rubber plantations is crucial to regional ecological security and a sustainable economy. However, the spectral characteristics of rubber tree is easily mixed with other vegetation such as natural forests, tea plantations, orchards and shrubs, which brings difficulty and uncertainty to regio

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Jiang, Liyuan, Yong Ma, Fu Chen, Jianbo Liu, Wutao Yao, and Erping Shang. "Automatic High-Accuracy Sea Ice Mapping in the Arctic Using MODIS Data." Remote Sensing 13, no. 4 (2021): 550. http://dx.doi.org/10.3390/rs13040550.

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The sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance bands is constructed to obtain sea ice data with a high temporal and spatial resolution. By constructing a training sample library and using a multi-feature fusion machine learning algorithm for model classification, the high-accuracy recognition of ice and cloud regions is achieved. The

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Norton, Cynthia L., Kyle Hartfield, Chandra D. Holifield Collins, Willem J. D. van Leeuwen, and Loretta J. Metz. "Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species." Remote Sensing 14, no. 12 (2022): 2896. http://dx.doi.org/10.3390/rs14122896.

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Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of m

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Wang, Y., X. Huang, and M. Gao. "3D MODEL OF BUILDING BASED ON MULTI-SOURCE DATA FUSION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-3/W2-2022 (October 27, 2022): 73–78. http://dx.doi.org/10.5194/isprs-archives-xlviii-3-w2-2022-73-2022.

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Abstract. The process of building a digital city is the construction of a three-dimensional city model. The construction of 3 d data model is the process of multi-source spatial information collection and fusion. The current means of spatial information collection are mainly divided into three-dimensional laser technology and UAV photogrammetry technology. The three-dimensional laser technology is mainly based on ground laser, which has high acquisition accuracy for the bottom part of the building, but it is insufficient for the information collection at the top of the building. UAV photogramm

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Ya’nan, Zhou, Luo Jiancheng, Feng Li, and Zhou Xiaocheng. "DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data." Remote Sensing 11, no. 13 (2019): 1619. http://dx.doi.org/10.3390/rs11131619.

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Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delinea

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Bayat, Nasrin, Diane D. Davey, Melanie Coathup, and Joon-Hyuk Park. "White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization." Big Data and Cognitive Computing 6, no. 4 (2022): 122. http://dx.doi.org/10.3390/bdcc6040122.

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Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific atte

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Wang, Dong, Jiahong Liu, Weiwei Shao, Chao Mei, Xin Su, and Hao Wang. "Comparison of CMIP5 and CMIP6 Multi-Model Ensemble for Precipitation Downscaling Results and Observational Data: The Case of Hanjiang River Basin." Atmosphere 12, no. 7 (2021): 867. http://dx.doi.org/10.3390/atmos12070867.

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Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and se

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Ma, Jingzhen, Qun Sun, Zhao Zhou, Bowei Wen, and Shaomei Li. "A Multi-Scale Residential Areas Matching Method Considering Spatial Neighborhood Features." ISPRS International Journal of Geo-Information 11, no. 6 (2022): 331. http://dx.doi.org/10.3390/ijgi11060331.

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Residential areas is one of the basic geographical elements on the map and an important content of the map representation. Multi-scale residential areas matching refers to the process of identifying and associating entities with the same name in different data sources, which can be widely used in map compilation, data fusion, change detection and update. A matching method considering spatial neighborhood features is proposed to solve the complex matching problem of multi-scale residential areas. The method uses Delaunay triangulation to divide complex matching entities in different scales into

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Li, Kai, Jinju Shao, and Dong Guo. "A Multi-Feature Search Window Method for Road Boundary Detection Based on LIDAR Data." Sensors 19, no. 7 (2019): 1551. http://dx.doi.org/10.3390/s19071551.

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In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-wa

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Yang, Funing, Guoliang Liu, Liping Huang, and Cheng Siong Chin. "Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data." Sensors 20, no. 21 (2020): 6046. http://dx.doi.org/10.3390/s20216046.

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Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order

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Yang, Liu, Hanxin Chen, Yao Ke, et al. "A novel time–frequency–space method with parallel factor theory for big data analysis in condition monitoring of complex system." International Journal of Advanced Robotic Systems 17, no. 2 (2020): 172988142091694. http://dx.doi.org/10.1177/1729881420916948.

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The spatial information of the signal is neglected by the conventional frequency/time decompositions such as the fast Fourier transformation, principal component analysis, and independent component analysis. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of parallel factor analysis, which is known as a unique multi-way decomposition. The parallel factor analysis was used to decompose the wavelet transformed ongoing diagnostic channel–frequency–time signal and each atom is trilinearly decomposed into spatial, spectral, and temporal

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Lin, Zhixian, Renhai Zhong, Xingguo Xiong, et al. "Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series." Remote Sensing 14, no. 3 (2022): 699. http://dx.doi.org/10.3390/rs14030699.

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Timely and accurate cropland information at large spatial scales can improve crop management and support the government in decision making. Mapping the spatial extent and distribution of crops on a large spatial scale is challenging work due to the spatial variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed in this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed a reasonable rice classification accuracy in the major rice production areas of the U.S. (OA = 98.3%, F1 score = 0.804), even when it only utiliz

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