The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2020-11-01)

SEMANTIC SEGMENTATION OF ENDANGERED TREE SPECIES IN BRAZILIAN SAVANNA USING DEEPLABV3+ VARIANTS

  • D. L. Torres,
  • R. Q. Feitosa,
  • L. E. C. La Rosa,
  • P. N. Happ,
  • J. Marcato Junior,
  • W. N. Gonçalves,
  • J. Martins,
  • V. Liesenberg

DOI
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-355-2020
Journal volume & issue
Vol. XLII-3-W12-2020
pp. 355 – 360

Abstract

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Knowing the spatial distribution of endangered tree species in a forest ecosystem or forest remnants is a valuable information to support environmental conservation practices. The use of Unmanned Aerial Vehicles (UAVs) offers a suitable alternative for this task, providing very high-resolution images at low costs. In parallel, recent advances in the computer vision field have led to the development of effective deep learning techniques for end-to-end semantic image segmentation. In this scenario, the DeepLabv3+ is well established as the state-of-the-art deep learning method for semantic segmentation tasks. The present paper proposes and assesses the use of DeepLabv3+ for mapping the threatened Dipteryx alata Vogel tree, popularly also known as cumbaru. We also compare two backbone networks for feature extraction in the DeepLabv3+ architecture: the Xception and MobileNetv2. Experiments carried out on a dataset consisting of 225 UAV/RGB images of an urban area in Midwest Brazil demonstrated that DeepLabv3+ was able to achieve in mean overall accuracy and F1-score above 90%, and IoU above 80%. The experimental analysis also pointed out that the MobileNetv2 backbone overcame its counterpart by a wide margin due to its comparatively simpler architecture in view of the available training data.