Fusion of Sentinel-1 and Sentinel-2 satellite images for detecting agricultural areas in Boa Vista do Tupim - BA.
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The aim of this study is to apply a decision-level spectral fusion technique to detect agricultural areas in the municipality of Boa Vista do Tupim - BA. The proposed approach is based on combining the probability maps generated from independent classifications of data acquired by the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Imager (MSI) sensors to assess the performance of classifying seven land use and land cover (LULC) categories present in the study area. Methodology involves image selection and pre-processing, digital classification using the Random Forest algorithm, generation of probability maps, and evaluation of classification results using accuracy, precision and recall metrics. The results showed the potential for combining images from radar and optical sensors to detect LULC classes in Boa Vista do Tupim, BA.
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.
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 imagem, Distrito agrotecnológico de Boa Vista do Tupim, Projeto Semear Digital, Sentinel-1 Synthetic Aperture Radar, Image fusion, Sensoriamento Remoto, Agricultura, Remote sensing, Agriculture