This study presents a novel, semi-automated approach for integrating decision rules and object-based image analysis (OBIA) methods for identifying and mapping karst zones and landforms. We developed a multi-resolution segmentation approach using an Approximate Gaussian function to compute the degree of fuzzy memberships of object-based features and applied it to Sentinel-2 satellite images and a digital elevation model. The object based features and decision rules were applied to identify and detect karst landforms in the semi-automated approach. The efficiency of each technique was examined based on two case studies in Takht-Soleiman and Parava-Biston in Iran using a fuzzy synthetic evaluation (FSE) approach and ground control points. The validation of the karst landform detection and delineation yielded high accuracies for the six prominent landforms, namely Dolin (96.8%), Ouvala (99.2%), Lapiez (95.1%), Canyon (98.3%), Polje (96.1%) and Karren (97.4%), respectively. Based on the research outcome, we conclude that the combined use of spatial (e.g. shape index, compactness, asymmetry), spectral (e.g. brightness, mean and standard deviation) and textural (grey-level co-occurrence matrix, GLCM) features allows us to detect and map karst landforms efficiently. This fuzzy rule object-based approach can enhance the accuracy of geomorphological and geological maps and allows for a regular update of the usually labor-intensive geological mapping campaigns.

本研究提出了一种新颖的半自动化方法，用于整合决策规则和基于对象的图像分析 (OBIA) 方法，以识别和绘制岩溶带和地形图。我们开发了一种多分辨率分割方法，使用近似高斯函数来计算基于对象的特征的模糊隶属度，并将其应用于 Sentinel-2 卫星图像和数字高程模型。应用基于对象的特征和决策规则，以半自动方法识别和检测喀斯特地貌。基于在伊朗 Takht-Soleiman 和 Parava-Biston 的两个案例研究，使用模糊综合评估 (FSE) 方法和地面控制点检查了每种技术的效率。喀斯特地貌检测和描绘的验证对六个突出的地貌产生了较高的准确性，即 Dolin (96.8%)、Ouvala (99.2%)、Lapiez (95.1%)、Canyon (98.3%)、Polje (96.1%) 和 Karren （97.4%），分别。根据研究结果，我们得出结论，空间（例如形状指数、紧凑性、不对称性）、光谱（例如亮度、均值和标准差）和纹理（灰度共生矩阵，GLCM）特征的组合使用使我们能够有效地检测和绘制喀斯特地貌。这种基于模糊规则对象的方法可以提高地貌和地质图的准确性，并允许定期更新通常劳动密集型的地质填图活动。根据研究结果，我们得出结论，空间（例如形状指数、紧凑性、不对称性）、光谱（例如亮度、均值和标准差）和纹理（灰度共生矩阵，GLCM）特征的组合使用使我们能够有效地检测和绘制喀斯特地貌。这种基于模糊规则对象的方法可以提高地貌和地质图的准确性，并允许定期更新通常劳动密集型的地质填图活动。根据研究结果，我们得出结论，空间（例如形状指数、紧凑性、不对称性）、光谱（例如亮度、均值和标准差）和纹理（灰度共生矩阵，GLCM）特征的组合使用使我们能够有效地检测和绘制喀斯特地貌。这种基于模糊规则对象的方法可以提高地貌和地质图的准确性，并允许定期更新通常劳动密集型的地质填图活动。