%0 Journal Article
%4 sid.inpe.br/plutao/2020/06.15.23.32.18
%2 sid.inpe.br/plutao/2020/06.15.23.32.19
%@doi 10.1080/10106049.2020.1773544
%@issn 1010-6049
%F lattes: 7484071887086439 3 DinizGamaAdam:2020:EvPoIn
%T Evaluation of polarimetry and interferometry of Sentinel-1A SAR data for land use and land cover of the Brazilian Amazon region
%D 2020
%9 journal article
%A Diniz, Juliana Maria Ferreira de Souza,
%A Gama, Fábio Furlan,
%A Adami, Marcos,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress anadinizengflorestal@gmail.com
%@electronicmailaddress fabio.furlan@inpe.br
%@electronicmailaddress marcos.adami@inpe.br
%B Geocarto International
%V 35
%P e1773544
%K Dual-polarimetric, interferometric coherence, machine learning algorithms, land cover mapping.
%X 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.
%@language en
%3 diniz_evaluatin.pdf