Comparative analysis of digital classifiers of Landsat‑8 images for thematic mapping procedures

Abstract

The objective of this work was to evaluate the performance of SVM and K‑NN digital classifiers for the object‑based classification on Landsat‑8 images, applied to mapping of land use and land cover of Alta Bacia do Rio Piracicaba‑Jaguari, in the state of Minas Gerais, Brazil. The pre‑processing step consisted of using radiometric conversion and atmospheric correction. Then the multispectral bands (30 m) were merged with the panchromatic band (15 m). Based on RGP compositions and field inspection, 15 land‑use and land‑cover classes were defined. For edge segmentation, the bounds were set to 10 and 60 for segmentation configuring and merging in the ENVI software. Classification was done using SVM and K‑NN. Both classifiers showed high values for the Kappa index (k): 0.92 for SVM and 0.86 for K‑NN, significantly different from each other at 95% probability. A major improvement was observed for SVM by the correct classification of different forest types. The object‑based classification is largely applied on high‑resolution spatial images; however, the results of the present work show the robustness of the method also for medium‑resolution spatial images.