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.

The coseismic slip on the main fault related to the 2017 Iran-Iraq Mw7.3 earthquake has been investigated by previous studies using DInSAR (differential interferometric synthetic aperture radar) ground deformation measurements. However, DInSAR observation is not sensitive to the ground deformation in the along-track (AT) direction. Therefore, only the one-dimensional (1D) DInSAR coseismic deformation field measurements, derived in the LOS (line-of-sight) direction of radar, was applied in source parameters estimation. To further improve the accuracy of the fault slip inversion, the 3D (three-dimensional) coseismic deformation fields were reconstructed in the first place, by a combined use of the DInSAR and MAI (multiple aperture InSAR) measurements. Subsequently, the LOS and 3D deformation data sets were used as the constraint respectively, to perform a two-step inversion scheme to find an optimal geometry and slip distribution on the main fault. The comparative analysis indicated that the 3D coseismic deformation data sets improved the inversion-accuracy by 30%. Besides, the fault invention results revealed a deep dislocation on a NNW trending fault (the strike is 352.63°) extending about 60 km, along the fault dips 14.76° to the ENE. The estimated seismic moment is 8.44 × 10¹⁹ Nm (Mw7.3), which is close to the solution provided by USGS (United States Geological Survey). The slip distributed at the depth between 12 and 18 km, and the peak slip of 6.53 m appears at the depth of 14.5 km left near the epicenter. Considering the geological structure in the earthquake region and fault source-parameters, it comes to a preliminary conclusion that the ZMFF (the Zagros Mountain Front fault) should be responsible for the earthquake. In general, this paper demonstrated the superiority of using the 3D coseismic deformation fields on source parameters estimation.