However, many of the rapid changes occurring in polar regions are difficult to monitor due to the trade-off between the temporal and spatial resolutions of existing satellite sensors (fine spatial resolution with a long revisit period; coarse resolution with a short revisit period) [4; 11]. For example, the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra/Aqua satellites can provide daily observations that facilitate the capture of rapid surface changes [4], but the coarse spatial resolution (250-1000 m) of MODIS sensors is often inadequate for monitoring the collapse of small glaciers or the disintegration of small icebergs. In contrast, the Landsat-8 OLI/Sentinel-2 MSI sensor has a higher spatial resolution (30 m/10 m) than MODIS, providing more details of the snow and ice surface changes. However, the 16-day/10-day revisit period of the Landsat-8 OLI/Sentinel-2 MSI sensor limits its application in the study of time-sensitive events, such as sea ice drift, which may evolve rapidly in a few days. Therefore, a sensor that provides high-resolution remote sensing data on a daily frequency or satellite constellations are needed for observing the rapid changes in polar regions. Launched on 12 September 2019 and developed through the collaboration between Beijing Normal University, Sun Yat-sen University, led by Shenzhen Aerospace Dongfanghong HIT Satellite Ltd., Ice Pathfinder (Code: BNU-1) is the first Chinese polar-observing microsatellite. It is in a sun-synchronous orbit (SSO) with an altitude of 739 km above Earth's surface, a semi-major axis of 7,116,914.419 m, an inclination of 98.5238 Degrees, and an eccentricity of 0.000220908. Weighing only 16 kg, BNU-1 carries an optical payload with a panchromatic band and four multispectral bands. The spatial resolution at the sub-satellite point is approximately 74 m from the ground. The wide swath of BNU-1 (744 km) provides a 5-day revisit period of polar regions up to 85\({}^{\circ}\) latitude. BNU-1 takes into account the balance between spatial resolution and revisit frequency, providing observations with finer spatial resolution than Terra/Aqua MODIS data and more revisit frequency than Landsat-8 OLI and Sentinel-2 MSI, benefiting the environmental monitoring of the polar regions. Also, the low cost of BNU-1 makes it financially feasible to construct a constellation observation system [12]. A five-satellite constellation system provides the ability to observe polar environmental elements on a daily basis with a spatial resolution finer than 100 m.  
Image geolocation is an essential process prior to the application of satellites. However, geolocation errors are commonly found in visible images. For example, the images from MODIS have a geolocation error of 1.3 km in the along-track direction and 1.0 km in the along-scan direction without correction [13]. Geolocation errors need to be corrected because they cause uncertainty in satellite data and have a serious impact on the applications of satellite data for environmental monitoring [14]. The geolocation errors are usually corrected by parametric and non-parametric correction models [13; 15; 16]. Both these models correct the errors of a satellite image by matching the CPs obtained from the target image (the image with geolocation errors) and the corresponding points from the reference image with high geolocation accuracy [17]. The parametric correction model corrects the errors by optimizing the inner and external orientation parameters in the geometric calibration model based on the differences between the CPs from the target and reference images [13; 15; 18], while the non-parametric model is performed by establishing the coordinates transformation model between coordinates of the target image and coordinates of the reference images based on the CPs [8; 11; 12]. Both the parametric and non-parametric correction models are highly dependent on the amount of CPs [17; 19; 20].