USING TIME SERIES IMAGE DATA TO IMPROVE THE GENERALIZATION CAPABILITIES OF A CNN - THE EXAMPLE OF DEFORESTATION DETECTION WITH SENTINEL-2

Abstract

Deforestation is considered one of the main causes of global warming and biodiversity reduction. Therefore, early detection of deforestation processes is of paramount importance to preserve environmental resources. Currently, there is plenty of research focused on detecting deforestation from satellite imagery using Convolutional Neural Networks (CNNs). Although these works yield remarkable results, most of them employ pairs of images and detect changes which occurred between the two image acquisition epochs only. Furthermore, these models tend to produce poor results when applied to new data in real-world scenarios. In this regard, an interesting research topic deals with the generalization capacity of the classifiers. CNN-based approaches combined with time series data can be a suitable framework to obtain classifiers that generalize better to new data. Image time series contain complementary information, representing different imaging conditions over time. This work addresses the transferability for detecting deforestation in different areas of the Amazon region, using Sentinel-2 time series and reference maps from PRODES project, which are not required to be synchronized. The results indicate that the classifier with time series data brings a substantial improvement in accuracy by taking advantage of the temporal information.