Efficient land desertification detection using a deep learning‐driven generative adversarial network approach: A case study

Summary Precisely detecting land cover changes aids in improving the analysis of dynamics landscape and plays an essential role mitigating effects desertification. Mainly, sensing desertification is challenging due to high correlation between like‐desertification events (e.g., deforestation). An efficient flexible deep learning approach introduced address detection through Landsat imagery. Essentially, a generative adversarial network (GAN)‐based detector designed for uncovering pixels influenced by changes. In this study, adopted features have been derived from multi‐temporal images incorporate multispectral information without considering image segmentation preprocessing. Furthermore, challenges, GAN‐based constructed based on desertification‐free then employed identify atypical associated with The GAN‐detection algorithm flexibly learns relevant linear nonlinear processes prior assumption data distribution significantly enhances detection's accuracy. detector's performance has assessed via optical arid area nearby Biskra Algeria. This region selected work because phenomena heavily impact it. Compared some state‐of‐the‐art methods, including Boltzmann machine (DBM), belief (DBN), convolutional neural (CNN), as well two ensemble models, namely, random forests AdaBoost, proposed offers superior discrimination deserted regions. Results show promising potential method also revealed that GAN‐driven outperforms methods.