Cropland mapping from Sentinel-2 time series data using object-based image analysis
暂无分享,去创建一个
[1] Stéphane Dupuy,et al. A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM) , 2017, Remote. Sens..
[2] C. Atzberger,et al. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe , 2016, Remote. Sens..
[3] Gilberto Câmara,et al. A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[4] David Morin,et al. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series , 2015, Remote. Sens..
[5] Gérard Dedieu,et al. Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery , 2015, Remote. Sens..
[6] Lin Yan,et al. Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction , 2015 .
[7] Mariana Belgiu,et al. Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[8] Xianhong Xie,et al. Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data , 2014 .
[9] L. Drăguţ,et al. Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[10] M. J. Pringle,et al. Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[11] Damien Arvor,et al. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil , 2011 .
[12] Yan Gao,et al. Optimal region growing segmentation and its effect on classification accuracy , 2011 .
[13] Patrick Bogaert,et al. Forest change detection by statistical object-based method , 2006 .
[14] S. K. McFeeters. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .
[15] Russell G. Congalton,et al. A review of assessing the accuracy of classifications of remotely sensed data , 1991 .
[16] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[17] Chongcheng Chen,et al. Winter wheat mapping combining variations before and after estimated heading dates , 2017 .
[18] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[19] L. Breiman. Random Forests , 2001, Machine Learning.
[20] Arno Schäpe,et al. Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .