Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/2564630F-4499-4663-B579-1AC4EDABBB08 | - |
| Αναγνωριστικό | http://www.mdpi.com/2072-4292/9/12/1259 | - |
| Αναγνωριστικό | https://doi.org/10.3390/rs9121259 | - |
| Γλώσσα | en | - |
| Μέγεθος | 19 pages | en |
| Τίτλος | Co-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning | en |
| Δημιουργός | Χατζηαντωνίου Ανδρομάχη | el |
| Δημιουργός | Chatziantoniou Andromachi | en |
| Δημιουργός | Petropoulos Georgios | en |
| Δημιουργός | Πετροπουλος Γεωργιος | el |
| Δημιουργός | Psomiadis Emmanouil | en |
| Εκδότης | MDPI | en |
| Περίληψη | This 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. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-03-16 | - |
| Ημερομηνία Δημοσίευσης | 2017 | - |
| Θεματική Κατηγορία | Classification | en |
| Θεματική Κατηγορία | SAR | en |
| Θεματική Κατηγορία | Sentinel 1 | en |
| Θεματική Κατηγορία | Sentinel 2 | en |
| Θεματική Κατηγορία | Support vector machines | en |
| Θεματική Κατηγορία | Wetlands | en |
| Βιβλιογραφική Αναφορά | A. Chatziantoniou, G. P. Petropoulos and E. Psomiadis, "Co-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning" Remote Sens., vol. 9, no. 12, Dec., 2017. doi:10.3390/rs9121259 | en |
Copyright © DIAS 2013
