The accurate land cover change detection is critical to improve the 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 article 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 multitemporal images and include multispectral information, and no prior image segmentation is required. The proposed method was evaluated on the publicly available remote sensing data using multitemporal 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, k-mean clustering algorithms, and one-class support vector machine. The comparative results showed that the VAE consistently outperformed the other models for detecting changes to the land cover, mainly deserted regions. This study also showed that VAE outperformed the state-of-the-art algorithms.

中文翻译：

---

通过 ETM-Landsat 卫星数据使用改进的基于变分自动编码器的方法进行荒漠化检测

准确的土地覆盖变化检测对于改善景观动态分析和有效缓解荒漠化问题至关重要。荒漠化检测是一个具有挑战性的问题，因为一些荒漠化案例与类似荒漠化现象（例如森林砍伐）之间的高度相似。本文提供了一种基于 Landsat 影像和变分自编码器 (VAE) 检测荒芜区域的有效方法。VAE 模型作为一种基于深度学习的模型，由于其无分布假设和卓越的非线性近似，在特征提取和建模方面受到了特别关注。此处，将 VAE 方法应用于光谱特征以检测受土地覆盖变化影响的像素。考虑的特征是从多时相图像中提取的，包括多光谱信息，不需要先验图像分割。使用从免费提供的 Landsat 程序中获取的多时相 Landsat 光学图像，对公开可用的遥感数据评估了所提出的方法。阿尔及利亚比斯克拉附近的干旱地区被选为研究区，因为众所周知，荒漠化现象对该地区的影响很大。对 VAE 模型进行了评估，并与受限玻尔兹曼机、深度学习模型和二元聚类算法进行了比较，包括 Agglomerative、BIRCH、期望最大化、k-mean 聚类算法和一类支持向量机。比较结果表明，VAE 在检测土地覆盖变化方面始终优于其他模型，主要是荒凉地区。这项研究还表明，VAE 的性能优于最先进的算法。