Enhancing change detection in multi-date images using a Multi-temporal Siamese Neural Network

Abstract

Satellite imagery’s temporal and spatial variability presents challenges for accurate change detection. To address this challenge, we propose a Multi-temporal Siamese Variational Auto-Encoder (MSVAE). MSVAE obtains latent representations by concatenating extracted features from multi-date images while sharing weights. This architecture combines the advantages of Siamese networks and variational auto-encoders (VAE), ensuring both spatial and temporal consistency of the extracted features for desertification detection. We conducted experiments in the arid regions of Tunisia using Landsat imagery and supervised classification techniques for desertification detection. The results demonstrated a classification accuracy of 98.46% for the proposed MSVAE, outperforming other models, such as the Multi-temporal Siamese Convolutional Neural Network (MSCNN) and the Multi-temporal Siamese Recurrent Neural Network (MSRNN).