An Unsupervised Change Detection Approach for Dense Satellite Image Time Series Using 3D CNN

Recent satellite missions have initiated a new era in the area of Satellite Image Time Series (SITS) analysis by providing a huge number of High Resolution (HR) spectral-temporal images. The availability of HR images opens a door to an unprecedented wide range of possibilities to produce and develop high resolution Land Cover (LC) and Land Cover Change (LCC) maps. The goal of this paper is to effectively use high spatio-temporal resolution images to generate LCC maps by defining a novel automatic and unsupervised deep learning method based on three-dimensional (3D) Convolutional Neural Network (CNN). The method extracts spatio-temporal information from long SITS by using a pre-trained 3D CNN, detects changes and locates them in space and time. Experiments have provided promising results over both Amazonia and Saudi Arabia in the period 2013–2017, and has been compared to the other well-known LCC detection method.