Large-scale automatic block adjustment from satellite to indoor photogrammetry

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.

Geometric positioning integrating optical satellite stereo imagery and a global database of ICESat-2 laser control points:A framework and key technologies

Block Adjustment(BA)is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery.For block adjustment with few ground-control points or without ground control,the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products.The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods,high positioning precision,and low acquisition cost,providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA.This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery(OSSI)and spaceborne laser altimeter data.Firstly,we elaborate the principle and necessity of this work through a literature review of existing methods.Then,the framework of our geo-positioning models.Secondly,four key technologies of the proposed model are expounded in order,including the acquisition and management of global Laser Control Points,the association of LCPs and OSSI,the block adjustment model combining LCPs with OSSI,and the accuracy estimation and quality control of the combined BA.Next,the combined BA experiment using Ziyuan-3(ZY-3)OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province,China.Experimental results prove that our method can automatically select LCPs with high accuracy.The elevation deviation of the combined BA eventually achieved the Mean Error(ME)of 0.06 m and the Root Mean Square Error(RMSE)of 1.18 m,much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment.A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.

Advances of geo-spatial intelligence at LIESMARS

The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.

Improving the performance of inverted organic solar cells by adjusting the concentration of precursor solution of Al-doped ZnO

Al-doped ZnO(AZO) has been used as an electron transport and hole blocking buffer layer in inverted organic solar cells(IOSCs). In this paper, the AZO morphology, optical and structural properties and IOSCs performance are investigated as a function of precursor solution concentration from 0.1 mol/L to 1.0 mol/L. We demonstrate that the device with 0.1 mol/L precursor concentration of AZO buffer layers enhances the short-circuit current and the fill factor of IOSCs simultaneously. The resulting device shows that the power conversion efficiency is improved by 35.6% relative to that of the 1.0 mol/L device, due to the improved surface morphology and transmittance(300–400 nm) of AZO buffer layer.