The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1875–1880, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1875-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1875–1880, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1875-2019

  05 Jun 2019

05 Jun 2019

COMPARISON OF PIXEL AND REGION-BASED APPROACHES FOR TREE SPECIES MAPPING IN ATLANTIC FOREST USING HYPERSPECTRAL IMAGES ACQUIRED BY UAV

G. T. Miyoshi1, N. N. Imai1,2, A. M. G. Tommaselli1,2, and E. Honkavaara3 G. T. Miyoshi et al.
  • 1Post Graduate Program in Cartographic Science, São Paulo State University (UNESP), Presidente Prudente-SP, Brazil
  • 2Dept. of Cartography, São Paulo State University (UNESP), Presidente Prudente-SP, Brazil
  • 3Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, P.O. Box 15, FI-02431 Masala, Finland

Keywords: Image classification, Random Forest, Atlantic Forest, Hyperspectral images

Abstract. The objective of this work was the comparison of two different classification approaches to detect four different tree species of a highly diverse tropical Atlantic Forest area. In order to achieve the objective, images were acquired with the Rikola hyperspectral camera onboard the UX4 UAV. The study area is in the Western part of São Paulo State, a tropical Atlantic Forest area protected by governmental laws, which contains areas already deforested in the past and which are currently in regeneration. The tested approaches were one based only in the pixel values and other one based in regions. After the image acquisition, the images were radiometrically and geometrically processed. In addition, an airborne laser scanning point cloud was used to calculate the canopy height model of the area, which was used to detect the individual tree crowns with the superpixels method. Those superpixels were used to the region-based classification and to feature extraction. A total of 28 features were extracted where 25 correspond to the spectral bands acquired with the Rikola camera and three correspond to the three first principal components of the images. The features were extracted from the 91 samples recognized during a field work. From the total of samples, 19 were separated to validate the classification results. The chosen classifier was the Random Forests and the results presented a kappa coefficient of 18.20% and 36.57% for the pixel-based and region-based classifications showing that the second one had a better performance.