Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery
暂无分享,去创建一个
Andrii Shelestov | Nataliia Kussul | Leonid Shumilo | Mykola Lavreniuk | Andrii Kolotii | Vladimir Vasiliev | Bohdan Yailymov | Hanna Yailymova | N. Kussul | A. Shelestov | H. Yailymova | B. Yailymov | A. Kolotii | M. Lavreniuk | L. Shumilo | V. Vasiliev
[1] Mariana Belgiu,et al. Random forest in remote sensing: A review of applications and future directions , 2016 .
[2] Alemayehu Midekisa,et al. Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing , 2017, PloS one.
[3] Albert Y. Zomaya,et al. Remote sensing big data computing: Challenges and opportunities , 2015, Future Gener. Comput. Syst..
[4] Ming-Yuan Cho,et al. Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems , 2017, Comput. Intell. Neurosci..
[5] M. Ettaouil,et al. New Modeling of Multilayer Perceptron Architecture Optimization with Regularization : An Application to Pattern Classification , 2022 .
[6] Nataliia Kussul,et al. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.
[7] Yuqi Bai,et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .
[8] R. Congalton,et al. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .
[9] Nihan Kahraman,et al. An Efficient Method to Optimize Multi-Layer Perceptron for Classification of Human Activities , .
[10] Nataliia Kussul,et al. Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[11] D. Lobell,et al. Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .
[12] C. Justice,et al. High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.
[13] Javier Gallego,et al. Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations , 2012 .
[14] Chaowei Yang,et al. Utilizing Cloud Computing to address big geospatial data challenges , 2017, Comput. Environ. Urban Syst..
[15] Francisco Javier Gallego,et al. Efficiency assessment of using satellite data for crop area estimation in Ukraine , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[16] Asamaporn Sitthi,et al. Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thailand , 2018 .
[17] Clement Atzberger,et al. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe , 2016, Remote. Sens..
[18] Steffen Fritz,et al. The Need for Improved Maps of Global Cropland , 2013 .
[19] N. Clinton,et al. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform , 2017 .
[20] Charles P. Staelin. Parameter selection for support vector machines , 2002 .
[21] Nataliia Kussul,et al. Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine , 2015 .
[22] Alexei Novikov,et al. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping , 2017, Front. Earth Sci..
[23] Stéphane Dupuy,et al. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity , 2016 .
[24] Nataliia Kussul,et al. The use of satellite data for agriculture drought risk quantification in Ukraine , 2016 .
[25] Mariana Belgiu,et al. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis , 2018 .
[26] Guido Lemoine,et al. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.