A Semi-Automatic Method for Extracting Small Ground Fissures from Loess Areas Using Unmanned Aerial Vehicle Images
A Semi-Automatic Method for Extracting Small Ground Fissures from Loess Areas Using Unmanned Aerial Vehicle Images
复制标题
利用无人机图像提取黄土地区小地裂缝的半自动方法
DOI：
10.3390/rs13091784
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发表时间：
2021
期刊：
影响因子：
5
通讯作者：
Wu Shuaiying
中科院分区：
文献类型：
--
作者：
Jia Hongguo;Wei Bowen;Liu Guoxiang;Zhang Rui;Yu Bing;Wu Shuaiying
基于遥感的地裂缝提取技术（例如图像分类、图像分割、特征提取）广泛应用于地质灾害监测和桥梁、大坝、高速公路、隧道等大型人工工程项目。然而，由于黄土地区地形复杂、纹理信息多样、地面目标边界分散，常规技术无法在黄土地区应用，导致提取出许多虚假的地裂缝目标。为了快速、准确地获取黄土地区地裂缝，本研究提出了利用无人机图像检测黄土地裂缝空间分布的数据处理方案。首先，采用匹配滤波器（MF）算法和高斯一阶导数（FDOG）算法进行图像卷积。然后开发了一种新方法来生成归一化卷积的响应矩阵，而不是灵敏度校正参数，可以有效地提取初始地裂缝候选。综合考虑方向、MF/FDOG模板数量和算法效率，得出合适的参数方案。图像分类步骤采用随机森林（RF）算法来创建用于去除非地裂缝特征的掩模文件。下一步，利用数学形态学中的hit-or-miss变换算法和滤波算法将不连续的地裂缝连接起来，去除面积远小于地裂缝面积的像素集，得到最终的二值地裂缝图像。实验结果表明，该方案能够充分解决传统方法因边缘信息丰富、纹理多样而无法准确提取地裂缝的问题，从而从黄土地区高分辨率图像中获得细小地裂缝的精确结果。
Remote sensing-based ground fissure extraction techniques (e.g., image classification, image segmentation, feature extraction) are widely used to monitor geological hazards and large-scale artificial engineering projects such as bridges, dams, highways, and tunnels. However, conventional technologies cannot be applied in loess areas due to their complex terrain, diverse textural information, and diffuse ground target boundaries, leading to the extraction of many false ground fissure targets. To rapidly and accurately acquire ground fissures in the loess areas, this study proposes a data processing scheme to detect loess ground fissure spatial distributions using unmanned aerial vehicle (UAV) images. Firstly, the matched filter (MF) algorithm and the first-order derivative of the Gaussian (FDOG) algorithm were used for image convolution. A new method was then developed to generate the response matrices of the convolution with normalization, instead of the sensitivity correction parameter, which can effectively extract initial ground fissure candidates. Directions, the number of MF/FDOG templates, and the efficiency of the algorithm are comprehensively considerate to conclude the suitable scheme of parameters. The random forest (RF) algorithm was employed for the step of the image classification to create mask files for removing non-ground-fissure features. In the next step, the hit-or-miss transform algorithm and filtering algorithm in mathematical morphology is used to connect discontinuous ground fissures and remove pixel sets with areas much smaller than those of the ground fissures, resulting in a final binary ground fissure image. The experimental results demonstrate that the proposed scheme can adequately address the inability of conventional methods to accurately extract ground fissures due to plentiful edge information and diverse textures, thereby obtaining precise results of small ground fissures from high-resolution images of loess areas.
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DOI：
--
发表时间：
1970
期刊：
--
影响因子：
--
作者：
I. Sobel
通讯作者：
I. Sobel
DOI：
10.1080/136588199241445
发表时间：
1999
期刊：
Int. J. Geogr. Inf. Sci.
影响因子：
--
作者：
H. Tveite
通讯作者：
H. Tveite
影响因子：
5
作者：
Hung, Calvin;Xu, Zhe;Sukkarieh, Salah
通讯作者：
Sukkarieh, Salah
DOI：
10.14569/ijacsa.2013.041210
发表时间：
2013
影响因子：
0.9
作者：
I. Khalifa;A. Aboutabl;G. Barakat
通讯作者：
I. Khalifa;A. Aboutabl;G. Barakat
影响因子：
5
作者：
Feng, Quanlong;Liu, Jiantao;Gong, Jianhua
通讯作者：
Gong, Jianhua