Change Detection for bi-temporal images classification based on Siamese Variational AutoEncoder and Transfer Learning

Chouikhi Farah, Ali Ben Abbes, Imed Riadh Farah

Keywords: Feature extraction, Variational Auto-Encoder, Change Detection, Siamese structure, Transfer Learning, Desertification

Abstract: Siamese structures empower Deep Learning (DL) models to increase their efficiency by learning how to extract the relevant temporal features from the input data. In this paper, a Siamese Variational Auto-Encoder (VAE) model based on transfer learning (TL) is applied for change detection (CD) using bi-temporal images. The introduced method is trained in a supervised strategy for classification tasks. Firstly, the suggested generative method utilizes two VAEs to extract features from bi-temporal images. Subsequently, concatenates them into a feature vector. To get a classification map of the source scene, the classifier receives this vector and the ground truth data as input. The source model is fine-tuned to be applied to the target scene with less ground truth data using a TL strategy. Experiments were carried out in two study areas in the arid regions of southern Tunisia. The obtained results reveal that the proposed method outperformed the Siamese Convolution Neural Network (SCNN) by achieving an accuracy of more than 98%, in the source scene, and increased the accuracy in the target scene by 1.25% by applying the TL strategy.

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