Journal of Geo-information Science >
Ground Fissure Extraction Method based on Improved Active Contour Model for UAV Images in Mining Areas
Received date: 2022-06-02
Revised date: 2022-07-29
Online published: 2023-02-25
Supported by
Fundamental Research Funds for the Central Universities(2021YCPY0113)
National Natural Science Foundation of China(42271368)
National Natural Science Foundation of China(41701504)
Accurate identification of ground fissures in mining areas is significant for disaster prevention, mitigation, and ecological environment restoration. In this study, a ground fissure extraction method is proposed based on the improved active contour model for UAV images in mining areas, aiming at accurately extracting ground fissures from high-resolution UAV images. Firstly, the Otsu algorithm was used to calculate the background and initial values of ground fissures as prior knowledge. Secondly, the extraction energy functions of the background and initial values of ground fissures were constructed and introduced into the traditional CV active contour model to enhance the pertinence of ground fissures extraction. Finally, ground fissures were extracted through the continuous evolution of the contour. Based on UAV images obtained in Dalai Nurg mining area, Inner Mongolia, the improved active contour model was used to extract ground fractures, and compared with traditional Canny edge detection algorithm, Support Vector Machine (SVM), Maximum Likelihood Method (MLM), and traditional CV active contour model methods for analysis and accuracy evaluation. The results show that the traditional Canny edge detection algorithm and traditional CV active contour model had the poor extraction in a small area with a single type of land cover. The improved active contour model, SVM, and MLM had achieved good results, and the improved active contour model method had the highest accuracy. In addition, in a large area with relatively complex land cover types, the traditional methods such as Canny edge detection algorithm, SVM, MLM, and CV active contour model had many omissions and errors, and the kappa coefficient was lower than 0.7. However, the improved active contour method still achieved better results, and the Kappa coefficient was about 0.9. Therefore, the proposed method could effectively improve the accuracy and stability of ground fissure extraction by introducing prior knowledge.
HAO Ming , LIN Huijing , GAO Yanyan . Ground Fissure Extraction Method based on Improved Active Contour Model for UAV Images in Mining Areas[J]. Journal of Geo-information Science, 2022 , 24(12) : 2448 -2457 . DOI: 10.12082/dqxxkx.2022.220376
图5 实验区域1改进主动轮廓模型、传统的Canny边缘检测算法、SVM模型、MLM模型和传统CV主动轮廓模型地裂缝提取结果对比Fig. 5 Comparison of ground fissure extraction results between improved active contour model, traditional Canny edge detection algorithm, SVM model, MLM model and traditional CV active contour model in experimental area 1 |
图6 实验区域2改进主动轮廓模型、传统的Canny边缘检测算法、SVM模型、MLM模型和传统CV主动轮廓模型地裂缝提取结果Fig. 6 Comparison of ground fissure extraction results between improved active contour model, traditional Canny edge detection algorithm, SVM model, MLM model and traditional CV active contour model in experimental area 2 |
表1 实验区域1精度评价结果Tab. 1 Accuracy evaluation results of experimental area 1 |
方法 | Kappa系数 | PMA/% | PFA/% | PTE/% |
---|---|---|---|---|
改进主动轮廓模型 | 0.9335 | 8.1197 | 0.1331 | 0.3491 |
SVM | 0.8497 | 20.4274 | 0.1972 | 0.7443 |
MLM | 0.8469 | 8.2906 | 0.6653 | 0.8715 |
表2 实验区域2精度评价结果Tab. 2 Accuracy evaluation results of experimental area 2 |
方法 | Kappa系数 | PMA/% | PFA/% | PTE/% |
---|---|---|---|---|
改进主动轮廓模型 | 0.8974 | 8.3941 | 0.2336 | 0.3915 |
SVM | 0.6835 | 8.4683 | 1.4350 | 1.5679 |
MLM | 0.5520 | 5.2433 | 2.7558 | 2.8034 |
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