Hybrid lightweight transformer for efficient landslide change detection in remote sensing imagery

Remote sensing images may be used to detect and segment landslide areas. Recently, encoder and decoder-based convolutional neural networks (CNN) have been used to localise landslide regions. However, CNN-based methods extract shallow spatial features and lack the ability to capture long range dependencies. Due to this, edges and boundary regions of landslides are less explored. In this study, we present the change detection convolutional transformer network (CDCTNet), a hybrid model designed using CNN and transformer-based attention to identify and segment landslides. CDCTNet contains a lightweight convolutional encoder for spatial feature extraction. Local self-attention (LSA) and global self-attention (GSA) blocks are incorporated into the transformer to provide local attention and global information exchange with minimal overhead. The decoder consists of convolution blocks and a gated attention block that maintains long-range dependencies and provides for high processing speed to generate localized landslide region masks. The proposed model is experimentally evaluated on the Debrisflow, Landslide and Bijie datasets and compared with six state-of-the-art (SOTA) methods. CDCTNet achieved F1-scores of 76.56%, 72.06% and 94.85%, respectively, on the three datasets, surpassing previous methods with less computation overhead. In addition, CDCTNet requires lower flops and fewer trainable parameters compared to the transformer, ensuring feasibility in cloud-based real-time applications.