Produção Nacional
Revisado por pares
Object-based image analysis supported by data mining to discriminate large areas of soybean
2018; Taylor & Francis; Volume: 12; Issue: 3 Linguagem: Inglês
10.1080/17538947.2017.1421722
ISSN1753-8955
AutoresCarlos Antônio da Silva, Marcos Rafael Nanni, José Francisco de Oliveira‐Júnior, Everson Cézar, Paulo Eduardo Teodoro, Rafael Coll Delgado, Luciano Shozo Shiratsuchi, Muhammad Shakir, Marcelo Luiz Chicati,
Tópico(s)Remote Sensing and LiDAR Applications
ResumoThis research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. Two major techniques were used: GEOgraphic-Object-Based Image Analysis (GEOBIA) and Data Mining (DM). In order to obtain the images, the segmentation algorithm implemented by Definiens Developer was used. A decision tree (DT) was created from a training set previously prepared. Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle. The time-series data were used only for the CEI index. Furthermore, to compare the results obtained from GEOBIA, the slicing technique was used at the CEI level. After the training, the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean. In accordance with the error matrix and kappa parameter analysis, tests for statistical significance were created. Results indicate that the classification achieved by Kappa coefficients is 0.76. In short, the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale.
Referência(s)