Multiannual Change Detection Using a Weakly Supervised 3-D CNN in HR SITS
Khatereh Meshkini, Francesca Bovolo, Lorenzo Bruzzone · IEEE Geoscience and Remote Sensing Letters · 2024
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.