Co-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learningCo-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2018-03-162017enThis study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes.http://creativecommons.org/licenses/by/4.0/Remote Sensing912Chatziantoniou_et_al_Remote Sens._9(12)_2017.pdfChania [Greece]Library of TUC2018-03-16application/pdf5.6 MBfree Χατζηαντωνίου Ανδρομάχη Chatziantoniou Andromachi Petropoulos Georgios Πετροπουλος Γεωργιος Psomiadis Emmanouil MDPI Classification SAR Sentinel 1 Sentinel 2 Support vector machines Wetlands