In this study, an intelligent algorithm for the extraction of sand dune maps from Landsat satellite images in terrestrial and coastal environments was presented by a case study of the sand dunes in northeastern Iran and one example on the coast of the Oman Sea in southeastern Iran. First, to find the best sand dune detection algorithm, the land cover map is created by aerial photography and field observation in ILWIS software. Next, a binary image is created in which the sand dunes are considered as white pixels and other parts as black pixels. In the next step, the pixels of each land cover map class are divided into 50 equal parts, one part of them is used to train different smart grids and the rest of them are used for evaluation. Then the classification is done using all 50 parts in turn, and the Correct Classification Rate (CCR) index is calculated. This action is performed for the K-Nearest Neighbor, Decision Tree, AdaBoost, RUSBoost, and SVM algorithms. Furthermore, to see the effect of the PCA and LDA statistical methods, the classifier algorithms are implemented by applying these techniques. Finally, the proposed method was tested in a coastal area as an example. The findings show that the CCR of the proposed method is around 84% using 2% of the ground truth of the area for training and the other 98% of them for testing. In the case study, the detection results show that around 9.98% of the area is covered by sand dunes. To have a practical detection of the sand dunes area, the proposed machine learning method is applied to some experimentally verified points. Based on the results, ensemble classification methods, especially RUSBoost, have the best detection accuracy. Also, the result of its test on the coastal sand dunes showed its ability to identify, map, and monitor the sand dunes in coastal areas.

在这项研究中，通过对伊朗东北部沙丘的案例研究和伊朗东南部阿曼海岸的一个实例，提出了一种从陆地和陆地环境中的Landsat卫星图像中提取沙丘图的智能算法。首先，为了找到最佳的沙丘检测算法，通过ILWIS软件中的航空摄影和野外观察来创建土地覆盖图。接下来，创建一个二进制图像，其中沙丘被视为白色像素，其他部分被视为黑色像素。下一步，将每个土地覆盖图类别的像素分为50个相等的部分，其中一部分用于训练不同的智能网格，其余部分用于评估。然后依次使用全部50个部分进行分类，并计算正确的分类率（CCR）指数。此动作是针对K最近邻居，决策树，AdaBoost，RUSBoost和SVM算法执行的。此外，为了查看PCA和LDA统计方法的效果，通过应用这些技术来实现分类器算法。最后，以沿海地区为例对提出的方法进行了测试。研究结果表明，所建议方法的CCR约为84％，其中使用2％的区域地面真实性进行训练，其余98％用于测试。在案例研究中，检测结果表明，大约9.98％的区域被沙丘覆盖。为了对沙丘区域进行实际检测，将提出的机器学习方法应用于一些经过实验验证的点。根据结果​​，整体分类方法，特别是RUSBoost，具有最佳的检测精度。此外，其对沿海沙丘的测试结果表明其具有识别，测绘和监视沿海地区沙丘的能力。