Desertification Detection using an Improved Variational AutoEncoder-Based Approach through ETM-Landsat Satellite Data

The accurate land cover change detection is critical to improve landscape dynamics analysis and mitigate desertification problems efficiently. Desertification detection is a challenging problem because of the high degree of similarity between some desertification cases and like-desertification phenomena, such as deforestation. This paper provides an effective approach to detect deserted regions based on Landsat imagery and Variational AutoEncoder (VAE). The VAE model, as a deep learning-based model, has gained special attention in features extraction and modeling due to its distribution-free assumptions and superior nonlinear approximation. Here, a VAE approach is applied to spectral signatures for detecting pixels affected by the land cover change. The considered features are extracted from multi-temporal images and include multi-spectral information, and no prior image segmentation is required. The proposed method was evaluated on the publicly available remote sensing data using multi-temporal Landsat optical images taken from the freely available Landsat program. The arid region around Biskra in Algeria is selected as a study area since it is well-known that desertification phenomena strongly influence this region. The VAE model was evaluated and compared with restricted Boltzmann machines, deep learning model, and binary clustering algorithms, including Agglomerative, Birch, expected maximization, KMean clustering algorithms, and one-class support vector machine. The comparative results showed that the VAE consistently outperformed the other models for detecting changes to land cover, mainly deserted regions. This study also showed that VAE outperformed the state of the art algorithms.