A convolutional neural network approach to the detection of LC transitions in multi-annual satellite image time series

Recently, deep learning-based methods have been exploited to learn complex features from Satellite Image Time Series (SITS) with superior spatial, spectral, and temporal resolution for the Land Cover Transition (LCT) analysis. However, in order to efficiently utilize High Resolution (HR) SITS for detecting LCTs, there is a need to tackle challenges related to a proper modelling of the LC behavior and pertain to the intricacy of the temporally dense SITS. A novel LCT detection approach is presented that exploits a pretrained Three Dimensional (3D) Convolutional Neural Network (CNN) to simultaneously extract spatio-temporal information from multi-annual SITS to identify the LCTs. To highlight the changed pixels, a multi-feature hyper temporal difference feature vector is generated that properly provides intrinsic information of the LC trends in space and time. To distinguish different LCTs between two consecutive years for the changed pixels, a clustering process is performed that considers the temporal information of the difference hyper features to discriminate and understand the LCTs. The product is a map indicating the location of changed pixels and providing information about the type of LCTs. The preliminary analysis has been done over a region in Sahel – Africa with images acquired between 2015 and 2016. The proposed approach has been compared with another LCT detection approach using 2D CNN. Experimental results confirm the effectiveness of the proposed approach in detecting the LCTs.