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

• Main Authors: D. L. Torres, R. Q. Feitosa, L. E. C. La Rosa, P. N. Happ, J. Marcato Junior, W. N. Gonçalves, J. Martins, V. Liesenberg
• Format: Article
• Language: English
• Published: Copernicus Publications 2020-11-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W12-2020/355/2020/isprs-archives-XLII-3-W12-2020-355-2020.pdf

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 <i>Dipteryx alata</i> 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.