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Cloud Computing in Remote Sensing: Big Data Remote Sensing Knowledge Discovery and Information Analysis
2021
International Journal of Advanced Computer Science and Applications
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This paper proposes a feature supporting, salable, and efficient data cube for time-series analysis application, and used the spatial feature data and remote sensing data for comparative study of the water ...
It uses the long time-series remote sensing production process and analysis as examples to evaluate the performance of a feature data cube and distributed execution engine. ...
Using this model, researchers can conveniently extract the desired data from the large dataset for analysis, and it reduces the burdens of data preparation for researchers in time-series analysis. ...
doi:10.14569/ijacsa.2021.01205104
fatcat:iczi4zladvfbdgaf2u4m43eyw4
Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest
2021
Remote Sensing
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In summary, a suitable combination of reducer functions for synthesizing annual time series images can enhance data quality and ensure differences between characteristics and higher precision for urban ...
Remote sensing has greatly advanced urban areas mapping over the last several decades. At present, we have entered the era of big data. ...
Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2021, 13, 748 ...
doi:10.3390/rs13040748
fatcat:5iubdfglp5cbhbgzceumwfi5qq
Remote Sensing Landslide Recognition Based on Convolutional Neural Network
2019
Mathematical Problems in Engineering
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Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. ...
Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input ...
Acknowledgments is study was supported by the National Key R&D Program of China (2016YFB0502502) and the National Natural Science Foundations of China (61871150). ...
doi:10.1155/2019/8389368
fatcat:p5kbjyp3ljdflmizgovvn3hd6e
Flow Cytometric Immunophenotypic Analysis in the Diagnosis and Prognostication of Plasma Cell Neoplasms
2019
Cytometry. Part B, Clinical cytometry
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The challenges of and strategies for evaluating plasma cells in the setting of targeted therapy are also highlighted. © 2019 International Clinical Cytometry Society. ...
This review focuses on the roles of flow cytometric immunophenotyping has in the differential diagnosis of plasma cell neoplasms and the post-therapy monitoring of minimal (measurable) residual disease ...
Acknowledgments is study was supported by the National Key R&D Program of China (2016YFB0502502) and the National Natural Science Foundations of China (61871150). ...
doi:10.1002/cyto.b.21844
pmid:31566910
fatcat:aid3veeftnhk7iijbs5tuve464
Heifangtai loess landslide type and failure mode analysis with ascending and descending Spot-mode TerraSAR-X datasets
2019
Landslides. Journal of the International Consortium on Landslides
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Finally, the deformation types and failure modes of landslides in the study sites are analyzed by jointly using the two-dimensional deformation rates and time series results, topographic map, remote sensing ...
Furthermore, the twodimensional deformation derived from InSAR technique can give much detailed deformation characteristics and movement of loess landslide. ...
Qiang Xu of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, for providing the three-meter spatial resolution UAV DEM. ...
doi:10.1007/s10346-019-01265-w
fatcat:topynzrmhrbqtgypttsoptahrq
The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection at a Regional Scale: The Case in Southeastern Colombia
[chapter]
2020
Slope Engineering [Working Title]
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This landslide detection research applied remote sensing techniques. Morphometry to derive both DEM terrain parameters and land use variables. ...
A detection model was implemented using the Random Forest supervised method relating the training sample of landslides with multidimensional explanatory variables. ...
NDVI time series analysis Time series analysis of the multi-year Landsat NDVI was used as input data for the change detection analysis. ...
doi:10.5772/intechopen.94251
fatcat:a4vpc7e5dvga5crkltovupysme
$M^3\text{Fusion}$ : A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion
2018
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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, named M 3 F usion, able to leverage simultaneously the temporal knowledge contained in time series data as well as the fine spatial information available in VHSR information. ...
Modern Earth Observation systems provide sensing data at different temporal and spatial resolutions. ...
Integration of information from the HSR time series Recently, recurrent neural network (RNN) approaches demonstrated their quality in the remote sensing field to produce land use mapping using time series ...
doi:10.1109/jstars.2018.2876357
fatcat:3hk3utc7v5ed3hcmflwm2ipvhe
Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data
2019
Sensors
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First, we extracted geo-parcels from optical images with high spatial resolution. ...
This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future. ...
., Ltd. and Institute of Agricultural Economics and Information Technology Ningxia Academy of Agriculture and Forestry Sciences, who provided the datasets. ...
doi:10.3390/s19194227
fatcat:mz5iply7hvftlawefbepm3tywm
Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
2021
Remote Sensing
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In this paper, we proposed a multidimensional assessment framework for evaluating water provisioning ecosystem services by integrating multi-source remote sensing products. ...
We applied the multidimensional framework to assess lake water ecosystem services in the state of Minnesota, US. ...
Acknowledgments: We thank the anonymous reviewers for their constructive comments that greatly improved the quality of our manuscript. ...
doi:10.3390/rs13173540
fatcat:udqokmdcu5bb5gtljlm7ehe4yy
The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data
2021
Water
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Scientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. ...
A myriad of file formats and organizational conventions exist for storing these array datasets. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/w13152066
fatcat:ybtdzano5rbz5hspkwan6xjfz4
M3Fusion: A Deep Learning Architecture for Multi-Scale/Modal/Temporal satellite data fusion
[article]
2018
arXiv
pre-print
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, named M3Fusion, able to leverage simultaneously the temporal knowledge contained in time series data as well as the fine spatial information available in VHSR information. ...
Modern Earth Observation systems provide sensing data at different temporal and spatial resolutions. ...
-20, as well as from the financial contribution from the Ministry of Agriculture's "Agricultural and Rural Development" trust account. ...
arXiv:1803.01945v1
fatcat:rhqvy4huufg7pa7bq7fpnkwq2i
Accurate Identification of Pine Wood Nematode Disease with a Deep Convolution Neural Network
2022
Remote Sensing
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The use of remote sensing methods can achieve macroscopic and dynamic detection of this disease; however, the efficiency and accuracy of traditional remote sensing image recognition methods are not always ...
for SqueezeNet's transfer learning on the sample dataset. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs14040913
fatcat:y2wtv67dwrfsjclkb5in246qjq
A High-Dimensional Indexing Model for Multi-Source Remote Sensing Big Data
2021
Remote Sensing
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It is critical to realize unified organization and to form data sharing service capabilities for massive remote sensing data effectively. ...
With continuous improvement of earth observation technology, source, and volume of remote sensing data are gradually enriched. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs13071314
fatcat:hv5he7gw5jgipllaek75nrpnka
Remote Sensing Time Series Classification Based on Self-Attention Mechanism and Time Sequence Enhancement
2021
Remote Sensing
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Conflicts of Interest: The authors declare no conflict of interest. ...
We arbitrarily took Landsat8 time series remote sensing data for a period of time, and visualized the time series of some samples. ...
We arbitrarily took Landsat8 time series remote sensing data for a period of time, and visualized the time series of some samples. ...
doi:10.3390/rs13091804
dblp:journals/remotesensing/LiuYWHHL21
fatcat:tfjn4yhqunhfjoslvccmx6egzi
Detection of Oil Spill Using SAR Imagery Based on AlexNet Model
2021
Computational Intelligence and Neuroscience
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The existing remote sensing images of the oil spills in recent years in China are used to build a dataset. ...
However, due to the limitation of its imaging characteristics, it is difficult to use traditional image processing methods to effectively extract oil spill information from SAR images with coherent speckle ...
For remote sensing images, remote sensing images have rich features. Different remote sensing images may have different characteristics of oil spills. ...
doi:10.1155/2021/4812979
fatcat:rqyeoo4xyndc5mgzu4kfojvrte
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