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ABSTRACT
Block Adjustment (BA) is a critical procedure in the geometric processing of satellite images, responsible for compensating and correcting the geometric positioning errors of the images. The accuracy of the photogrammetric products, including Digital Orthophoto Map (DOM), Digital Elevation Model (DEM), Digital Line Graphic (DLG), and Digital Raster Graphic (DRG), directly depends on the accuracy of BA results. In recent years, the rapid development of related technologies such as Artificial Intelligence (AI), Computer Vision (CV), Unmanned Aerial Vehicles (UAVs) and big data has greatly facilitated and transformed the classical BA in photogrammetry. This paper first reviews the current status of BA and then looks into the future. First, this paper provides a brief review of the key technologies involved in BA, including image matching, the establishment of adjustment model, the determination of the parameters and the detection of gross error. Then, taking the intercross and fusion of current technologies such as AI, cloud computing and big data with photogrammetry into account, this paper explores the future trends of photogrammetry. Finally, four typical cases of large-scale adjustment are introduced, including large-scale BA without Ground Control Points (GCPs) for optical stereo satellite images, large-scale BA with laser altimetry data for optical stereo satellite images, large-scale BA for UAV oblique photogrammetry, and large-scale BA for indoor photogrammetry in caves with a large number of close-range images.
Acknowledgments
The ZY-3 satellite data with nationwide coverage and the associated processing environment utilized in this study were provided by the China Center for Resources Satellite Data and Application (CRESDA). We express our sincere appreciation to CRESDA for their invaluable support in this case research.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The ZY-3 stereo imagery data that support the findings of this study are restricted in access and use, but can be queried and requested from the website of the China Centre for Resources Satellite Data and Application (CRESDA) (http://www.cresda.com/EN/) and Chinese Natural Resources Satellite Remote Sensing Cloud Service Platform (http://sasclouds.com/chinese/normal/). The GLAS/ICESat data are openly available and can be acquired from the National Snow & Ice Data Center (NSIDC) at https://nsidc.org/data/.
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Notes on contributors
Deren Li
Deren Li is a professor and chair of the Academic Committee of the State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University. He was selected as a member of Chinese Academy of Sciences in 1991 and a member of Chinese Academy of Engineering in 1994. He got his PhD degree from University of Stuttgart, Germany. He was awarded the title of honorary doctor from ETH, Switzerland in 2008. He has been awarded the Brock Gold Medal in recognition of outstanding contribution to photogrammetry in 2020. His research interests include photogrammetry and remote sensing, global navigation satellite system, geographic information system, and their innovation integrations and applications.
Bo Yang
Bo Yang is a professor at the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University. He received the PhD degree from Wuhan University in 2014. His research interests include automatic match, change detecting, and high-precision geometric processing of remote sensing imagery.
Mi Wang
Mi Wang is a professor at the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University. He received the PhD degree from Wuhan University in 2001. His research interests include high-resolution remote sensing satellite ground processing and the integration and rapid update of photogrammetry and GIS.
Taiping Wang
Mi Wang is a professor at the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University. He received the PhD degree from Wuhan University in 2001. His research interests include high-resolution remote sensing satellite ground processing and the integration and rapid update of photogrammetry and GIS.
Taiping Wang received the B.Eng. and M.Sc. degrees in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2018 and 2021, respectively. He is currently pursuing the PhD degree at the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing at Wuhan University. His research interests include high precision remote sensing image processing and the processing of spaceborne laser altimetry data.
Yunlong Gao
Yunlong Gao is a postdoc at the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University. He received his B.E. and Ph.D. degrees in Remote Sensing and Photogrammetry from the Wuhan University in 2010 and 2017. His research interests lie in the field of photogrammetry, particularly including 3D reconstruction, image processing as well as their applications in digital twin cities, digitalization of antique and museum.
Yingdong Pi
Yingdong Pi received the B.Eng. and M.Sc. and Ph.D. degrees in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2014, 2017, and 2021, respectively. Now, he is a Postdoctoral Fellow of the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing at Wuhan University. His research interests include high precision remote sensing image processing and deep space detection.