Object-oriented extraction method for loess sinkholes based on deep learning and integrated terrain features

Abstract: A loess sinkhole is a typical geological hazard that is widely distributed in the Loess Plateau of China. However, some latent damages are difficult to detect in the natural hazard. Particularly, the serious damage caused by loess sinkholes is ever increasing, as the infrastructure rapidly developed in recent years. The resulting water and soil loss can aggravate and even trigger the occurrence, development of mudflows, collapses, and landslides. In addition, the sinkhole collapse can also seriously damage the farmland infrastructure, such as terraces, water storage facilities, and irrigation systems, as well as the infrastructures such as highways, railways, bridges, and oil and gas pipelines. Thus, it is a high demand to detect the loess sinkholes for the soil and water conservation and engineering construction in the loess regions. However, the traditional field surveys are costly and inefficient on the loess sinkholes. In this study, an automatic and object-oriented extraction of loess sinkholes was proposed to determine the influence of integrated terrain features on the extraction accuracy of the Convolutional Neural Networks (CNN) model. The study area was selected as Lanzhou City, Gansu Province in western China. The segmentation scale of the image was then determined by the Moran's I and Gray Level Co-occurrence Matrix entropy using WorldView 3 remote sensing image and ALOS digital elevation model data. An object-oriented extraction was implemented to obtain the spectrum (i.e., Mean_R, Mean_G, Mean_B, Std_R, Std_G, Std_B, Max_diff, and Brightness), shape (i.e., Area, Asymmetry, Border_index, Border_Length, Compactness, Density, Elliptic_Fit, Length/Width, Length, Shape_index, Roundness and Width), texture (i.e., GLCM_Homogeneity, GLCM_Contrast, GLCM_Mean, GLCM_Dissimilarity, GLCM_Entropy, and GLCM_Std), and terrain (i.e., Mean_DEM, Mean_Slope, Mean_Hillshade, Std_DEM, Std_Slope, and Std_Hillshade) features of the loess sinkholes. Two types of training samples were constructed with/without the terrain features, and then to train two CNN models for the extraction of the loess sinkholes in the same area. After that, the extraction accuracy of the model was evaluated, according to the precision rate, recall rate, and F1 score. A Support Vector Machine (SVM) model was also established to compare with the two CNN models. The research results show that the CNN model trained by integrating terrain features presented an accuracy rate of 94.62%, a recall rate of 86.27%, and an F1 score of 90.26%. Specifically, the false positive was significantly reduced, while the accuracy rate and F1 score increased by 18.10 percentage points, and 9.15 percentage points, respectively. The F1 scores of the two CNN models were both over 80%, which were 6.94 percentage points, and 16.09 percentage points higher than that of the SVM model, respectively. Consequently, the integrated terrain features to train the CNN model can effectively reduce the number of False Positive in the loess sinkholes, thus improving the comprehensive performance of the model. The extraction performances of the CNN models were all better than those of the SVM model. Therefore, the CNN model can be widely expected to integrate with the terrain features from the satellite data and object-oriented image analysis for the accurate and efficient extraction of the loess sinkholes.