Evaluation of polarimetry and interferometry of sentinel-1A SAR data for land use and land cover of the Brazilian Amazon Region. Issue 5 (4th March 2022)

• Record Type: Journal Article
• Authors: Diniz, Juliana Maria Ferreira de Souza; Gama, Fabio Furlan; Adami, Marcos
• Is Part Of: Geocarto international. Volume 37:Issue 5(2022)
• Page Start: 1482
• Page End: 1500
• Publication Date: 2022-03-04
• Subjects: Dual-polarimetric -- interferometric coherence -- machine learning algorithms -- land cover mapping; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2020.1773544
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Synthetic aperture radar (SAR) data has been an alternative for monitoring ground targets, especially in areas with cloud cover. This study evaluates the potential of Sentinel-1A attributes for mapping land use and land cover (LULC) in a region of the Brazilian Amazon, using two different machine learning classifiers: Random Forest (RF) and Support Vector Machine (SVM). Different scenarios were used that combined backscattering, polarimetry, and interferometry to the classification process, which was divided into two phases to improve the results. The RF shows superiority over the SVM for almost all scenarios for the two phases of the mapping. The scenario with all data, presented the best results with both classifiers. The final maps with RF and SVM, obtained a global accuracy of 82.7% and 74.5%, respectively. This study demonstrated the potential of Sentinel-1 to map LULC classes in the Amazon region using a classification in two phases.