Assessment of an Early Fusion CNN Approach applied to the deforestation detection in the Brazilian Amazon

Deforestation is one of the main causes of biodiversity reduction, climate change among others destructive phenomena. Thus, early detection of deforestation processes is of paramount importance in the recent year. Motivated by this scenario the present work focuses on assessing a DL approach called Early Fusion (EF) for automatic deforestation detection. Change detection approaches based on Random Forest (RF) and Change Vector Analysis (CVA) were adopted as baselines for comparison purposes. These approaches were evaluated in a region located in the state of Pará, Brazil, where two images from Landsat 8 satellite were acquired to detect deforested areas from 2016 to 2017. Their corresponding references were collected from the Satellite Deforestation Monitoring Project in the Legal Amazon (PRODES). In the experiments, the EF approach outperformed RF and CVA baselines, identifying in a better way the regions that have suffered deforestation.