A Tie Point Matching Strategy for Very High Resolution Sar-Optical
Stereogrammety Over Urban Areas

Qiu, Chunping; Schmitt, Michael; Zhu, Xiao Xiang

doi:10.5194/isprs-archives-xlii-1-w1-411-2017

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…For a more detailed and application-oriented evaluation of similarity measures for SAR-optical image matching, we use these similarity measures in the SAR-optical stereogrammetry framework proposed in [8]. Similar to the experiments described in [8], two very high resolution spaceborne datasets acquired by TerraSAR-X and Worldview-2, respectively, over the city of Munich, Germany, are used. The height interval for constructing the IMBLS window was set to [h 0 − 5m, h 0 + 20m], where h 0 was taken from the SRTM DEM of the study area.…”

Section: Application To Sar-optical Stereogrammetrymentioning

confidence: 99%

“…In contrast to these full-fledged pipelines, which comprise a number of different processing steps, we intend to focus purely on similarity measures that can be used to identify tie points corresponding to each other across both image domains. As a framework for this investigation, we rely on the matching procedure for SAR-optical stereogrammetry proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

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Comparative evaluation of signal-based and descriptor-based similarity measures for SAR-optical image matching

Qiu

Schmitt

Zhu

2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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This paper compares different similarity measures for the matching of very-high-resolution SAR and optical images over urban areas. It is meant to provide guidance about the performance of both signal-based and descriptor-based similarity measures in the context of this non-trivial case of multi-sensor correspondence matching. Using an automatically generated training dataset, thresholds for the distinction between correct matches and wrong matches are determined. It is shown that descriptor-based similarity measures outperform signal-based similarity measures significantly.

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Section: Application To Sar-optical Stereogrammetrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of signal-based and descriptor-based similarity measures for SAR-optical image matching

Qiu

Schmitt

Zhu

2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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show abstract

“…Image matching has various applications. For example, it is used for tie point determination in relative orientation and Digital Surface Model (DSM) generation [9,30,47]. It can also be used in image registration [11,38,42,49].…”

Section: Introductionmentioning

confidence: 99%

Using Total Probability in Image Template Matching

Sadeq

2019

IAJIT

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Image template matching is a main task in photogrammetry and computer vision. The matching can be used to automatically determine the 3D coordinates of a point. A firstborn image matching method in fields of photogrammetry and computer vision is area-based matching, which is based on correlation measuring that uses normalised cross-correlation. However, this method fails at a discontinuous edge and at the area of low illumination or at geometric distortion because of changes in imaging location. Thus, these points are considered outliers. The proposed method measures correlations, which is based on normalised cross-correlation, at each point by using various sizes of window and then considering the probability of correlations for each window. Thereafter, the determined probability values are integrated. On the basis of a specific threshold value, the point of maximum total probability correlation is recognised as a corresponding point. The algorithm is applied to aerial images for Digital Surface Model (DSM) generation. Results show that the corresponding points are identified successfully at different locations, especially at a discontinuous point, and that a Digital Surface Model of high resolution is generated.

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“…Only recently, [10] showed that TerraSAR-X and GeoEye-1 images can be used to carry out stereogrammetric 3D reconstruction with an error in the meter-domain -although the study only used the manually measured corners of a simple-shaped building to proof the concept. In contrast, based on the preliminary considerations sketched in [11,12], our work intends to provide a first step towards a solution for the non-trival problem of automatic stereo matching of VHR multisensor images of complex urban areas. In this context, it combines SAR-optical stereo intersection with a fully automatic selection of sparsely distributed tie points, i.e.…”

Section: Introductionmentioning

confidence: 99%

Towards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery

Qiu

Schmitt

Zhu

2018

ISPRS Journal of Photogrammetry and Remote Sensing

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In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established hand-crafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging.

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A Tie Point Matching Strategy for Very High Resolution Sar-Optical Stereogrammety Over Urban Areas

Cited by 4 publications

References 14 publications

Comparative evaluation of signal-based and descriptor-based similarity measures for SAR-optical image matching

Comparative evaluation of signal-based and descriptor-based similarity measures for SAR-optical image matching

Using Total Probability in Image Template Matching

Towards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery

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