LAND COVER CLASSIFICATION WITH SUPPORT VECTOR MACHINE APPLIED TO MODIS IMAGERY Paulo Gonçalves1 Hugo Carrão2 André Pinheiro2 Mário Caetano2 1 INRIA Rhône-Alpes, France 2 Instituto Geográfico Português, Portugal 15/11/2018 25th EARSeL Symposium, Porto 2005

Motivations and objectives Automatic land cover classification, from moderate resolution reflectance imagery MODIS data specificities: - Multi-spectral reflectances: 7 bands (459nm 2155nm) Multi-temporal aquisition: weekly composite of daily images over one year - Spatial resolution: 500 meters - Radiometric corrections and geolocated calibration - Vegetation indices: NDVI and EVI Methodology: Support Vector Machine (SVM) 15/11/2018 25th EARSeL Symposium, Porto 2005

Classes nomenclature and sampling 9 land cover classes based on CORINE Land Cover 2000 nomenclature: - Water areas - Urban areas - Bare soils - Natural grasslands - Shrub lands - Needleleaf forests - Broadleaf forests - Non irrigated lands - Irrigated lands 15/11/2018 25th EARSeL Symposium, Porto 2005

Classes nomenclature and sampling 9 land cover classes based on CORINE Land Cover 2000 nomenclature: - Water areas (15) - Urban areas (30) - Bare soils (15) - Natural grasslands (30) - Shrub lands (30) - Needleleaf forests (30) - Broadleaf forests (30) - Non irrigated lands (31) - Irrigated lands (30) Sample collection based on CORINE Land Cover 2000 cartography and Landsat ETM+ images 15/11/2018 25th EARSeL Symposium, Porto 2005

25th EARSeL Symposium, Porto 2005 MODIS data processing toolbox (Matlab®) 15/11/2018 25th EARSeL Symposium, Porto 2005

Time sliding windowed median filter Window width 25th EARSeL Symposium, Porto 2005

Cloud corrected profiles Band 2 NDVI EVI Band 3 Band 7 Band 4 Band 5 Band 6 15/11/2018 25th EARSeL Symposium, Porto 2005

Support Vector Machine Supervised machine learning system (Vapnik et al.,92) 1) Non-linear mapping of data into higher dimensional space 2) Linear separation in feature space Non-linear classification in input space Exhaustive benchmarks with standard classifiers exist (Nearest Neighbors, Neural Networks, Maximum Likelihood, Classification Trees...) What kind of beast is this??? 15/11/2018 25th EARSeL Symposium, Porto 2005

25th EARSeL Symposium, Porto 2005 Support Vector Machine 15/11/2018 25th EARSeL Symposium, Porto 2005

25th EARSeL Symposium, Porto 2005 Global classification performances Experimental protocol Principal components analysis with full rank representation (9 dim) Cross validation to estimate generalized classification error (5 folds) Single date measurements K-NN SVM Generalized classification errors as functions of time 15/11/2018 25th EARSeL Symposium, Porto 2005

25th EARSeL Symposium, Porto 2005 Class identification accuracy User Producer 15/11/2018 25th EARSeL Symposium, Porto 2005

25th EARSeL Symposium, Porto 2005 Partial conclusions and future work Land Cover Classification Optimal date for MODIS-based Land Cover classification Need to revise Classes Definition and Nomenclature Need to collect new samples sets for Training and for Testing Classification Methodology Promising application of SVM learning systems Fit the times series with (parsimonious) parametric models (for data fusion and dimensionality reduction) Use model parameters as inputs of SVM classifiers 15/11/2018 25th EARSeL Symposium, Porto 2005

Producer's Accuracy (%) Class identification accuracy Classification Reference Data   W NG BLF BS SL NLF IL NIL U User's Accuracy (%) 13 1 92.9 18 2 5 3 52.9 25 86.2 10 76.9 24 61.5 92.6 22 73.3 26 Producer's Accuracy (%) 86.7 60.0 83.3 66.7 80.0 71.0 15/11/2018 25th EARSeL Symposium, Porto 2005