The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 21–28, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-21-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 21–28, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-21-2022
 
30 May 2022
30 May 2022

AUTOMATIC ALGORITHM FOR GEOREFERENCING HISTORICAL-TO-NOWADAYS AERIAL IMAGES ACQUIRED IN NATURAL ENVIRONMENTS

D. Craciun and A. Le Bris D. Craciun and A. Le Bris
  • LASTIG, Univ. Gustave Eiffel, IGN, ENSG, F-94160 Saint-Mandé, France

Keywords: patch-based feature-less aerial image matching, Histogram of Gradients, Digital Surface Model (DSM), automated aerial multi-epoch georeferencing

Abstract. Automatic georeferencing for historical-to-nowadays aerial images represents the main ingredient for supplying territory evolution analysis and environmental monitoring. Existing georeferencing methods based on feature extraction and matching reported successful results for multi-epoch aerial images acquired in structured and man-made environments. While improving the state-of- the-art of the multi-epoch georeferencing problem, such frameworks present several limitations when applied to unstructured scenes, such as natural feature-less environments, characterized by homogenous or texture-less areas. This is mainly due to the lack of structured areas which often results in sparse and ambiguous feature matches, introducing inconsistencies during the pose estimation process. This paper addresses the automatic georeferencing problem for historical aerial images acquired in unstructured natural environments. The research work presented in this paper introduces a feature-less algorithm designed to perform historical-to- nowadays image matching for pose estimation in a fully automatic fashion. The proposed algorithm operates within two stages: (i) 2D patch extraction and matching and (ii) 3D patch-based local alignment. The final output is a set of 3D patch matches and the 3D rigid transformation relating each homologous patches. The obtained 3D point matches are designed to be injected into traditional multi-views pose optimisation engines. Experimental results on real datasets acquired over Fabas area situated in France demonstrate the effectiveness of the proposed method. Our findings illustrate that the proposed georeferencing technique provides accurate results in presence of large periods of time separating historical from nowadays aerial images (up to 48 years time span).