## 1 Introduction  
Coal, being one of the primary global energy sources, plays a pivotal role in driving economic and social development. Nevertheless, coal mining induces rock strata deformation and surface ground subsidence, giving rise to an array of environmental and geological challenges that jeopardize the wellbeing and property safety of local inhabitants. As the coal mine backing face advances, a subsidence basin gradually takes shape on the surface, constituting a complex spatial and temporal phenomenon [1]. Hence, it is imperative to monitor surface deformations within the mining area, investigate subsidence patterns, and forecast mining-induced subsidence. These efforts will yield essential scientific guidance for ensuring safe coal mining practices and effectively mitigating and managing geological hazards in the mining region [2]. Surface settlement monitoring conventionally entails the establishment of observation stations along the primary strike and dip of the working face. It utilizes traditional measurement methods, including triangulation, wireline measurement, leveling, and GNSS, to monitor distinct mining stages and acquire deformation data for the measurement points via data processing. The conventional monitoring technology exhibits high measurement accuracy and widespread application in mine surface deformation monitoring. Nevertheless, it also presents significant limitations: limited \"point-like\" data are acquired from surface observation stations, resulting in low spatial resolution; substantial manpower and material resources are consumed; the process is time-consuming, and observation stations are vulnerable to damage. The rapid advancements in remote sensing technology have significantly facilitated the integration of synthetic aperture radar (InSAR) and unmanned aerial vehicle (UAV) technologies for deformation monitoring. InSAR technology captures the elevation information of a specific area through interferometric processing of two SAR images. Differential synthetic aperture radar interferometry (D-InSAR), an extension of InSAR, incorporates an external digital elevation model (DEM) to monitor ground surface deformation [3,4]. D-InSAR offers various advantages, including extensive coverage, high accuracy, and spatial continuity, making it highly sensitive to small ground surface deformations. The theoretical observation accuracy can reach centimeter or even millimeter levels, enabling the detection of subtle deformations at the edges of sedimentation basins in mining areas. However, despite these benefits, D-InSAR has limitations when it comes to monitoring large deformations in the central region of mine subsidence basins due to the presence of decoherence, which results in inaccurate deformation detection. To address this, [PERSON] et al. [5] achieved centimeter-level monitoring of surface deformation using InSAR, marking a significant breakthrough in its application to deformation monitoring. [PERSON] et al. [6] demonstrated the potential of the D-InSAR technique by using three ERS-1 images for monitoring mine deformations, especially in mine subsidence monitoring. Consequently, numerous scholars have engaged in research based on conventional InSAR techniques, proposing methods like Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), and Temporal Coherence Point InSAR (TCP-InSAR), among others [4,7,8,9,10,11,12], and have applied them to case studies in mining areas, achieving millimeter-level observation accuracy. However, these conventional methods still face challenges in accurately monitoring large-gradient deformations in mining regions. In recent years, UAV photogrammetry technology has experienced rapid development, with UAVs becoming increasingly lightweight and affordable. Simultaneously, computer performance has greatly improved, leading to enhanced data processing efficiency for UAVs. These advancements create an opportunity for the application of UAVs in surface deformation monitoring within mining areas.