Cropland mapping from Sentinel-2 time series data using object-based image analysis

[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 .