Special Issue "Advances in Photogrammetry and Remote Sensing: Data Processing and Innovative Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Roland Perko
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Guest Editor
JOANNEUM RESEARCH Forschungsgesellschaft mbH, DIGITAL, Remote Sensing and Geoinformation, 8010 Graz, Austria
Interests: photogrammetry; computer vision; remote sensing; machine learning
Prof. Dr. Mattia Crespi
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Guest Editor
Geodesy and Geomatics Division—DICEA, Sapienza University of Rome, Italy
Interests: remote sensing big data analysis; optical and SAR satellite remote sensing, photogrammetry, and stereo-SAR; 3D terrain and objects modeling; GNSS positioning and monitoring; GNSS seismology
Special Issues and Collections in MDPI journals
Dr. Karsten Jacobsen
Website
Guest Editor
Leibniz Universität Hannover, Institute for Photogrammetry and Geoinformation, 30167 Hannover, Germany
Interests: photogrammetry; remote sensing; height models

Special Issue Information

Dear Colleagues,

Photogrammetric remote sensing is evolving, especially with the introduction of novel optical and SAR sensors and due to novel processing methods and innovative applications. Methods from computer vision, machine learning, and deep learning influence remote-sensing-based metrology and foster novel applications—for example, forest assessment, city modeling, land cover and land use classification, carbon reporting, farm land monitoring, change detection, glacier observation, flood prediction, coastal mapping, determination of subsidence, or disaster damage mapping. The driving power are advances in photogrammetry and in remote sensing, allowing the generation of higher-quality source material from, for instance, stereo matching, 3D reconstruction, neural networks on 2D images and on 3D point clouds.

This Special Issue aims to collect papers discussing such advances and breakthroughs in photogrammetric remote sensing. Submitted manuscripts should mainly focus on novelties introduced by recent approaches that link photogrammetry and remote sensing, for example, with the following topics:

  • 3D remote sensing with SAR and optical sensors;
  • Image orientation and geo-referencing;
  • Discrete 3D representation of the surface of the Earth;
  • Digital surface, elevation and terrain models (DSMs, DEMs, DTMs);
  • Forest assessment;
  • City modeling;
  • Land cover and land use classification;
  • Carbon reporting;
  • Farm land monitoring;
  • Change detection;
  • Glacier observation;
  • Flood prediction;
  • Coastal mapping;
  • Determination of subsidence;
  • Disaster damage mapping.

Dr. Roland Perko
Prof. Mattia Crespi
Dr. Karsten Jacobsen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Photogrammetry
  • Remote sensing
  • Computer vision
  • Machine learning
  • Deep learning

Published Papers (1 paper)

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Research

Open AccessArticle
Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras
Remote Sens. 2020, 12(8), 1240; https://doi.org/10.3390/rs12081240 - 13 Apr 2020
Abstract
The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Control Points [...] Read more.
The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Control Points (GCPs) are not available or the camera system cannot be properly calibrated. This paper presents a new workflow called Point Cloud Stacking (PCStacking) that overcomes these restrictions by making the most of the iterative solutions in both camera position estimation and internal calibration parameters that are obtained during bundle adjustment. The basic principle of the stacking algorithm is straightforward: it computes the median of the Z coordinates of each point for multiple photogrammetric models to give a resulting PC with a greater precision than any of the individual PC. The different models are reconstructed from images taken simultaneously from, at least, five points of view, reducing the systematic errors associated with the photogrammetric reconstruction workflow. The algorithm was tested using both a synthetic point cloud and a real 3D dataset from a rock cliff. The synthetic data were created using mathematical functions that attempt to emulate the photogrammetric models. Real data were obtained by very low-cost photogrammetric systems specially developed for this experiment. Resulting point clouds were improved when applying the algorithm in synthetic and real experiments, e.g., 25th and 75th error percentiles were reduced from 3.2 cm to 1.4 cm in synthetic tests and from 1.5 cm to 0.5 cm in real conditions. Full article
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