Integration of the SAR and optical sensors of the Sentinel constellation for land use classification in Lagoinha (SP).
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Information on land use and coverage is necessary to assist in the management process and assertive decision-making. Thus, the present study aimed to evaluate the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) data in the mapping of land use and coverage of the municipality of Lagoinha (SP) using the Random Forest method. Three scenarios were tested for classification: data from (S1), (S2) and fusion of (S2+S1). To evaluate the accuracy of the classification, high-resolution images from Google Earth and S2 software were used. The overall accuracy of the classification from the combination of S2+S1 data was 94%, and the Kappa index was equal to 0.9. For the isolated images of S2 and S1, overall accuracies of 80% and 50% and Kappas index of 0.71 and 0.50 were obtained, respectively. The fusion of S1+S2 data showed high accuracy in mapping.
Na publicação: Luciana Alvim Romani. Organização: Silvia Maria Fonseca Silveira Massruhá, Durval Dourado Neto, Luciana Alvim Santos Romani, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Ivan Bergier, Maria Angelica de Andrade Leite, Vitor Del Alamo Guarda, Catarina Barbosa Careta.
Na publicação: Luciana Alvim Romani. Organização: Silvia Maria Fonseca Silveira Massruhá, Durval Dourado Neto, Luciana Alvim Santos Romani, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Ivan Bergier, Maria Angelica de Andrade Leite, Vitor Del Alamo Guarda, Catarina Barbosa Careta.
Palabras clave
Fusão de sensores, Aprendizado de máquina, Machine learning, Sensoriamento Remoto, Radar, Uso da Terra, Remote sensing, Sensors (equipment), Land use
