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Peter Reinartz

Researcher at German Aerospace Center

Publications -  413
Citations -  7428

Peter Reinartz is an academic researcher from German Aerospace Center. The author has contributed to research in topics: Change detection & Hyperspectral imaging. The author has an hindex of 37, co-authored 399 publications receiving 6017 citations. Previous affiliations of Peter Reinartz include Qatar Airways & University of Osnabrück.

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Journal ArticleDOI

Mutual-Information-Based Registration of TerraSAR-X and Ikonos Imagery in Urban Areas

TL;DR: A solely histogram-based method to achieve automatic registration within TerraSAR-X and Ikonos images acquired specifically over urban areas is analyzed and indicates that the proposed method is successful in estimating large global shifts followed by a fine refinement of registration parameters for high-resolution images acquired over dense urban areas.
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Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models

TL;DR: This paper proposes a change detection method based on stereo imagery and digital surface models generated with stereo matching methodology and provides a solution by the joint use of height changes and Kullback-Leibler divergence similarity measure between the original images.
Journal ArticleDOI

3D change detection – Approaches and applications

TL;DR: This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest.
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Airborne Vehicle Detection in Dense Urban Areas Using HoG Features and Disparity Maps

TL;DR: An integrated real-time processing chain which utilizes multiple occurrence of objects in images is described which has been verified using image sections from two different flights and manually extracted ground truth data from the inner city of Munich.
Book ChapterDOI

Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery

TL;DR: Zhang et al. as discussed by the authors proposed a new method consisting of a joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features, which are fed into rotation-based region proposal and region of interest networks to produce object detections.