Correction of Pushbroom Satellite Imagery Interior Distortions Independent of Ground Control Points

Zhang, Guo; Kai, Xu; Zhang, Qingjun; Li, Deren

doi:10.3390/rs10010098

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…Since the space environment is complex and variable during satellite launch and operation [15,28,30,31], the geographic coordinates calculated by geometric calibration models with the pre-launch laboratory measurement parameters usually have geolocation errors of about several hundred meters to several kilometers [32,33]. In addition, the random error of the attitude measurement cannot be eliminated due to the lack of ground control points in polar regions.…”

Section: Automated Geometric Correction Processing Methodsmentioning

confidence: 99%

Accuracy Evaluation on Geolocation of the Chinese First Polar Microsatellite (Ice Pathfinder) Imagery

et al. 2021

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Ice Pathfinder (Code: BNU-1), launched on September 12, 2019, is the first Chinese polar observation microsatellite. Its main payload is a wide-view camera with a ground resolution of 74 m at the subsatellite point and a scanning width of 744 km. 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 frequent revisits than Landsat-8 OLI and Sentinel-2 MSI. It is a valuable supplement for polar observations. Geolocation is an essential step in satellite image processing. This study aims to geolocate BNU-1 images; this includes two steps. For the first step, a geometric calibration model is applied to transform the image coordinates to geographic coordinates. The images calibrated by the geometric model are the Level1A (L1A) product. Due to the inaccuracy of satellite attitude and orbit parameters, the geometric calibration model also exhibits errors, resulting in geolocation errors in the BNU-1 L1A product. Then, a geometric correction method is applied as the second step to find the control points (CPs) extracted from the BNU-1 L1A product and the corresponding MODIS images. These CPs are used to estimate and correct geolocation errors. The BNU-1 L1A product corrected by the geometric correction method is processed to the Level1B (L1B) product. Although the geometric correction method based on CPs has been widely used to correct the geolocation errors of visible remote sensing images, it is difficult to extract enough CPs from polar images due to the high reflectance of snow and ice. In this study, the geometric correction employs an image division and an image enhancement method to extract more CPs from the BNU-1 L1A products. The results indicate that the number of CPs extracted by the division and image enhancements increases by about 30% to 182%. Twenty-eight images of Antarctica and fifteen images of Arctic regions were evaluated to assess the performance of the geometric correction. The average geolocation error was reduced from 10 km to ~300 m. In general, this study presents the geolocation method, which could serve as a reference for the geolocation of other visible remote sensing images for polar observations.

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Section: Automated Geometric Correction Processing Methodsmentioning

confidence: 99%

Accuracy Evaluation on Geolocation of the Chinese First Polar Microsatellite (Ice Pathfinder) Imagery

et al. 2021

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“…In the field of computer vision, camera autonomous calibration methods have always been a hot research topic [21][22][23][24]. In the field of satellite remote sensing, on-orbit autonomous geometric calibration methods have also attracted more and more attention from researchers in recent years [17,[25][26][27][28]. Gresloud et al [25], on the basis of the agile imaging capabilities of the Pleiades [29], used two imaging methods of along-track push-broom and cross-track swing-scanning to obtain cross images with a yaw angle difference of nearly 90 • during a satellite transit.…”

Section: Introductionmentioning

confidence: 99%

“…Although this method greatly reduces the need for ground control points compared with the traditional on-orbit geometric calibration method based on the ground calibration field, there is still a gap with the on-orbit autonomous geometric calibration method (no ground control points at all) we pursue. Zhang et al [27] proposed using at least three images with overlapping regions to match homologous points and calculate the internal distortion of the instrument according to the forward intersection residual between the homologous points. However, this method only considers the calibration of the internal distortion of the spaceborne push-broom instrument.…”

Section: Introductionmentioning

confidence: 99%

On-Orbit Autonomous Geometric Calibration of Directional Polarimetric Camera

Xiang

Meng

et al. 2022

Remote Sensing

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The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km).

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“…In the optical remote sensing field, many scholars have researched methods for geometric self-calibration that do not rely on control data, achieving notable success [15,16]. In the field of SAR geometric calibration, researchers have also begun to study geometric calibration without field calibration control data.…”

Section: Introductionmentioning

confidence: 99%

Geometric Self-Calibration of YaoGan-13 Images Using Multiple Overlapping Images

Zhang

Deng

Zhao

2019

Sensors

Self Cite

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Geometric calibration is an important means of improving the absolute positioning accuracy of space-borne synthetic aperture radar imagery. The conventional calibration method is based on a calibration field, which is simple and convenient, but requires a great deal of manpower and material resources to obtain ground control points. Although newer cross-calibration methods do not require ground control points, calibration accuracy still depends on a periodically updated reference image. Accordingly, this study proposes a geometric self-calibration method based on the positioning consistency constraint of conjugate image points to provide rapid and accurate calibration of the YaoGan-13 satellite. The proposed method can accurately calibrate geometric parameters without requiring ground control points or high-precision reference images. To verify the absolute positioning accuracy obtained using the proposed self-calibration method, YaoGan-13 Stripmap images of multiple regions were collected and evaluated. The results indicate that high-accuracy absolute positioning can be achieved with a plane accuracy of 3.83 m or better for Stripmap data, without regarding elevation error. Compared to the conventional calibration method using high-accuracy control data, the difference between the two methods is only about 2.53 m, less than the 3-m resolution of the image, verifying the effectiveness of the proposed self-calibration method.

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Correction of Pushbroom Satellite Imagery Interior Distortions Independent of Ground Control Points

Cited by 9 publications

References 32 publications

Accuracy Evaluation on Geolocation of the Chinese First Polar Microsatellite (Ice Pathfinder) Imagery

Accuracy Evaluation on Geolocation of the Chinese First Polar Microsatellite (Ice Pathfinder) Imagery

On-Orbit Autonomous Geometric Calibration of Directional Polarimetric Camera

Geometric Self-Calibration of YaoGan-13 Images Using Multiple Overlapping Images

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