Abstract

Abstract. Automated generation of digital elevation models (DEMs) from high resolution satellite images (HRSIs) has been an active research topic for many years. However, stereo matching of HRSIs, in particular based on image-space search, is still difficult due to occlusions and building facades within them. Object-space matching schemes, proposed to overcome these problem, often are very time consuming and critical to the dimensions of voxels. In this paper, we tried a new least square matching (LSM) algorithm that works in a 3D object space. The algorithm starts with an initial height value on one location of the object space. From this 3D point, the left and right image points are projected. The true height is calculated by iterative least squares estimation based on the grey level differences between the left and right patches centred on the projected left and right points. We tested the 3D LSM to the Worldview images over 'Terrassa Sud' provided by the ISPRS WG I/4. We also compared the performance of the 3D LSM with the correlation matching based on 2D image space and the correlation matching based on 3D object space. The accuracy of the DEM from each method was analysed against the ground truth. Test results showed that 3D LSM offers more accurate DEMs over the conventional matching algorithms. Results also showed that 3D LSM is sensitive to the accuracy of initial height value to start the estimation. We combined the 3D COM and 3D LSM for accurate and robust DEM generation from HRSIs. The major contribution of this paper is that we proposed and validated that LSM can be applied to object space and that the combination of 3D correlation and 3D LSM can be a good solution for automated DEM generation from HRSIs.

Highlights

  • Understanding the exact shape of the earth surface has been one of the primary goals of the modern science

  • We report our work on automated digital elevation models (DEMs) generation from stereo pairs of high resolution satellite images (HRSIs)

  • We proposed a new least squares matching scheme based on the 3D object space

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Summary

INTRODUCTION

Understanding the exact shape of the earth surface has been one of the primary goals of the modern science. The true height is calculated by iterative least squares estimation based on the grey levels of the left and right patches centred on the projected left and right points This 3D LSM can overcome the problems of the image space matching since the projected image points will not lie on the singularities. For a 3D object point with a given height, its image positions in the left and right images are calculated and the left and right patches at different resolutions are generated and used for LSM. This multi-layered matching scheme may decrease mis-matching rates as lower resolution image patches prevent mismatches. We determine the optimum sigma value is 0.2 times of search window size by experiments

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