ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume IV-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 83–90, 2018
https://doi.org/10.5194/isprs-annals-IV-3-83-2018
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 83–90, 2018
https://doi.org/10.5194/isprs-annals-IV-3-83-2018

  23 Apr 2018

23 Apr 2018

AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

Peter Fischer1, Philipp Schuegraf2, Nina Merkle1, and Tobias Storch1 Peter Fischer et al.
  • 1Remote Sensing Technology Institute, German Aerospace Center (DLR), M¨unchener Str. 20, 82234 Wessling, Germany
  • 2Dept. of Scientific Computing, University of Applied Science Munich, Lothstr. 20, Munich, Germany

Keywords: Evolutionary Algorithm, Image Matching, Tie Point Search, Multi-Sensor Matching, Co-Registration

Abstract. This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.