A Semi-Automatic Method for Extracting Small Ground Fissures from Loess Areas Using Unmanned Aerial Vehicle Images

Hongguo Jia,Rui Zhang,Guoxiang Liu,Shuaiying Wu,Bing Yu,Bowen Wei

doi:10.3390/rs13091784

Abstract

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.

Highlights

We propose an advanced scheme of ground fissure extraction b ground fissures” and accuracy assessment of linear targets
The use of a dilation operation in the mathematical morphology for the boundaries can eliminate the effect of this phenomenon
We combined six types of textural information, which were created by a 7 × 7 co-occurrence matrix, as well as the spectral information, to calculate the required parameters of the random forest (RF) algorithm by sample training

Summary

Introduction

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Methods

Results

Conclusion