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Spatially Regularized Fusion of Multiresolution Digital Surface Models

Kuschk, Georg and d'Angelo, Pablo and Gaudrie, David and Reinartz, Peter and Cremers, Daniel (2017) Spatially Regularized Fusion of Multiresolution Digital Surface Models. IEEE Transactions on Geoscience and Remote Sensing, 55 (3), pp. 1477-1488. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2016.2625040 ISSN 0196-2892

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Official URL: http://ieeexplore.ieee.org/document/7752839/?reload=true

Abstract

In this paper, we propose an algorithm for robustly fusing digital surface models (DSMs) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. Robust fusion of the DSMs is achieved by minimizing the L1-distance of each pixel of the solution to each input DSM. This approach is similar to a pixel-wise median, and most outliers are discarded. We further incorporate local planarity assumption as an additional constraint to the optimization problem, thus reducing the noise compared with pixel-wise approaches. The optimization is also inherently able to include weights for the input data, therefore allowing to easily integrate invalid areas, fuse multiresolution DSMs, and to weight the input data. The complete optimization problem is constructed as a variational optimization problem with a convex energy functional, such that the solution is guaranteed to converge toward the global energy minimum. An efficient solver is presented to solve the optimization in reasonable time, e.g., running in real time on standard computer vision camera images. The accuracy of the algorithms and the quality of the resulting fused surface models are evaluated using synthetic data sets and spaceborne data sets from different optical satellite sensors.

Item URL in elib:https://elib.dlr.de/118291/
Document Type:Article
Title:Spatially Regularized Fusion of Multiresolution Digital Surface Models
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kuschk, GeorgTUMhttps://orcid.org/0000-0002-6217-2241
d'Angelo, Pablopablo.angelo (at) dlr.dehttps://orcid.org/0000-0001-8541-3856
Gaudrie, DavidDLRUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Cremers, DanielTUMUNSPECIFIED
Date:March 2017
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:55
DOI :10.1109/TGRS.2016.2625040
Page Range:pp. 1477-1488
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Signal to noise ratio, Optimization, Optical sensors, Image resolution, Robustness, Surface reconstruction
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Angelo, Dr. Pablo
Deposited On:18 Jan 2018 13:45
Last Modified:31 Jul 2019 20:15

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