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

2019; Copernicus Publications; Volume: IV-2/W7; Linguagem: Inglês

10.5194/isprs-annals-iv-2-w7-121-2019

2194-9050

M. X. Ortega, José David Bermúdez Castro, Patrick Nigri Happ, Alessandra Rodrigues Gomes, Raul Queiroz Feitosa,

Remote-Sensing Image Classification

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.