Multiannual Change Detection Using a Weakly Supervised 3-D CNN in HR SITS
Khatereh Meshkini, Francesca Bovolo, Lorenzo Bruzzone
Abstract
In recent years, deep learning methods, in particular Convolutional Neural Networks (CNNs), have been increasingly used in Change Detection (CD). However, most CNN-based CD methods are primarily designed for analyzing only a single pair of images due to the challenge of collecting and constructing ground reference data during the system-training phase. Consequently, existing CD methods, particularly those focused on detecting multi-annual changes, exhibit limited capability in extracting comprehensive spatio-temporal information. To address this limitation, we propose a novel weakly supervised deep learning-based technique for CD exploiting a 3D CNN architecture to extract spatio-temporal information. Our technique incorporates a fine-tuning stage to effectively capture temporal patterns from a yearly Satellite Image Time Series (SITS) by using different 3D convolutional layers. It also exploits a multi-feature hyper-temporal Change Vector Analysis (CVA) for multi-annual change identification. The proposed approach is tested on a four year dataset in Amazonia and gained the highest yearly CD accuracy of 88.59%, 97.27% and 87.87% for 2017, 2018 and 2019, respectively.