ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume IV-2/W7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 121–128, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-121-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 121–128, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-121-2019

  16 Sep 2019

16 Sep 2019

EVALUATION OF DEEP LEARNING TECHNIQUES FOR DEFORESTATION DETECTION IN THE AMAZON FOREST

M. X. Ortega1, J. D. Bermudez1, P. N. Happ1, A. Gomes2, and R. Q. Feitosa1,3 M. X. Ortega et al.
  • 1Pontifical Catholic University of Rio de Janeiro, Brazil
  • 2National Institute for Space Research (INPE), São José dos Campos, Brazil
  • 3Rio de Janeiro State University, Rio de Janeiro-RJ, Brazil

Keywords: Deep learning, Convolutional Neural Networks, Image Stacking, Deforestation, Amazon Rainforest

Abstract. Deforestation is one of the main causes of biodiversity reduction, climate change among other destructive phenomena. Thus, early detection of deforestation processes is of paramount importance. Motivated by this scenario, this work presents an evaluation of methods for automatic deforestation detection, specifically Early Fusion (EF) Convolutional Network, Siamese Convolutional Network (S-CNN) and the well-known Support Vector Machine (SVM), taken as the baseline. These methods were evaluated in a region of the Brazilian Legal Amazon (BLA). Two Landsat 8 images acquired in 2016 and 2017 were used in our experiments. The impact of training set size was also investigated. The Deep Learning-based approaches clearly outperformed the SVM baseline in our approaches, both in terms of F1-score and Overall Accuracy, with a superiority of S-CNN over EF.