Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Browser

Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 9714

Share This Special Issue

Special Issue Editors

Prof. Dr. Fabio Remondino

E-Mail Website
Guest Editor

3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
Interests: photogrammetry; laser scanning; optical metrology; 3D; AI; quality control
Special Issues, Collections and Topics in MDPI journals

Dr. Devrim Akca

E-Mail Website
Guest Editor

Department of Civil Engineering, Isik University, TR-34980 Istanbul, Turkey
Interests: photogrammetry; laser scanning; camera calibration; machine vision
Special Issues, Collections and Topics in MDPI journals

Dr. Rongjun Qin

E-Mail Website
Guest Editor

1. Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA
2. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA
Interests: data analytics; aerial/satellite photogrammetry; remote sensing; image processing; machine learning; 3D computer vision; 3D modeling/change detection; deformation analysis; unmanned aerial vehicles; image dense matching
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photogrammetry is the science of producing reliable and precise information about physical objects and environment scenes by acquiring, processing and interpreting images. Photogrammetry uses images at different scales (from satellite to aerial, terrestrial and under water) and from different sensors (linear, frame, panoramic), aiming at accurate 3D measurements and products, notwithstanding automation. Photogrammetry has adopted in recent years various methods from the Computer Vision and Robotics communities and this has led to an increased level of automation in image processing and 3D data generation. Finally, the recent introduction of Artificial Intelligence methods (Machine and Deep Learning) has again revolutionized some photogrammetric processes leading to unexpected automated solutions in the 3D mapping field.

This Special Issue is dedicated to Prof Armin Gruen, a pioneer in many photogrammetric methods, a great mentor for many young scientists around the world and an initiator of various research projects related to automation, feature extraction, online triangulation, geometrically constrained multi-image least-squares image matching, city modeling, tracking, heritage documentation, etc. The Special Issue aims to collect state-of-the-art papers, research developments and innovative results related to Photogrammetry and its sister techniques. In particular, the following topics should be addressed in the proposed submissions:

Image matching and tie points extraction
Image orientation and bundle adjustment
Camera calibration
Least squares methods
Statistical methods, reliability studies and blunder detection in photogrammetric problems
Dense point cloud generation
Point clouds editing, cleaning and filtering
Automation in feature extraction from images
3D city modeling
Close-range photogrammetry
UAV photogrammetry
Airborne photogrammetry
Spaceborne photogrammetry

Dr. Fabio Remondino
Dr. Devrim Akca
Dr. Rongjun Qin
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 submissions that pass pre-check are 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 2700 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
least squares
image matching
3D reconstruction
quantitative analyses
optical remote sensing
cultural heritage

Published Papers (8 papers)

Download All Papers

Research

Jump to: Review

19 pages, 5854 KiB

Open AccessArticle

Urban Visual Localization of Block-Wise Monocular Images with Google Street Views

by Zhixin Li, Shuang Li, John Anderson and Jie Shan

Remote Sens. 2024, 16(5), 801; https://doi.org/10.3390/rs16050801 - 25 Feb 2024

Viewed by 620

Abstract

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day. Full article

(This article belongs to the Special Issue Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday)

► Show Figures

Figure 1

20 pages, 5848 KiB

Open AccessArticle

A Bi-Radial Model for Lens Distortion Correction of Low-Cost UAV Cameras

by Frank Liebold, David Mader, Hannes Sardemann, Anette Eltner and Hans-Gerd Maas

Remote Sens. 2023, 15(22), 5283; https://doi.org/10.3390/rs15225283 - 08 Nov 2023

Cited by 1 | Viewed by 858

Abstract

Recently developed cameras in the low-cost sector exhibit lens distortion patterns that cannot be handled well with established models of radial lens distortion. This study presents an approach that divides the image sensor and distortion modeling into two concentric zones for the application of an extended radial lens distortion model. The mathematical model is explained in detail and it was validated on image data from a DJI Mavic Pro UAV camera. First, the special distortion pattern of the camera was examined by decomposing and analyzing the residuals. Then, a novel bi-radial model was introduced to describe the pattern. Eventually, the new model was integrated in a bundle adjustment software package. Practical tests revealed that the residuals of the bundle adjustment could be reduced by 63% with respect to the standard Brown model. On the basis of external reference measurements, an overall reduction in the residual errors of 40% was shown. Full article

► Show Figures

Figure 1

25 pages, 14653 KiB

Open AccessArticle

2OC: A General Automated Orientation and Orthorectification Method for Corona KH-4B Panoramic Imagery

by Zhuolu Hou, Yuxuan Liu, Li Zhang, Haibin Ai, Yushan Sun, Xiaoxia Han and Chenming Zhu

Remote Sens. 2023, 15(21), 5116; https://doi.org/10.3390/rs15215116 - 26 Oct 2023

Viewed by 1054

Abstract

Due to a lack of geographical reference information, complex panoramic camera models, and intricate distortions, including radiation, geometric, and land cover changes, it can be challenging to effectively apply the large number (800,000+) of high-resolution Corona KH-4B panoramic images from the 1960s and 1970s for surveying-related tasks. This limitation hampers their significant potential in the remote sensing of the environment, urban planning, and other applications. This study proposes a method called 2OC for the automatic and accurate orientation and orthorectification of Corona KH-4B images, which is based on generalized control information from reference images such as Google Earth orthophoto. (1) For the Corona KH-4B panoramic camera, we propose an adaptive focal length variation model that ensures accuracy and consistency. (2) We introduce a robust multi-source remote sensing image matching algorithm, which includes an accurate primary orientation estimation method, a multi-threshold matching enhancement strategy based on scale, orientation, and texture (MTE), and a model-guided matching strategy. These techniques are employed to extract high-accuracy generalized control information for Corona images with significant geometric distortions and numerous weak texture areas. (3) A time-iterative Corona panoramic digital differential correction method is proposed. The orientation and orthorectification results of KH-4B images from multiple regions, including the United States, Russia, Austria, Burkina Faso, Beijing, Chongqing, Gansu, and the Qinghai–Tibet Plateau in China, demonstrate that 2OC not only achieves automation but also attains a state-of-the-art level of generality and accuracy. Specifically, the standard deviation of the orientation is less than 2 pixels, the mosaic error of orthorectified images is approximately 1 pixel, and the standard deviation of ground checkpoints is better than 4 m. In addition, 2OC can provide a longer time series analysis of data from 1962 to 1972, benefiting various fields such as environmental remote sensing and archaeology. Full article

► Show Figures

Figure 1

20 pages, 17728 KiB

Open AccessArticle

Highly Efficient Anchor-Free Oriented Small Object Detection for Remote Sensing Images via Periodic Pseudo-Domain

by Minghui Wang, Qingpeng Li, Yunchao Gu and Junjun Pan

Remote Sens. 2023, 15(15), 3854; https://doi.org/10.3390/rs15153854 - 03 Aug 2023

Viewed by 958

Abstract

With the continuous progress of remote sensing image object detection tasks in recent years, researchers in this field have gradually shifted the focus of their research from horizontal object detection to the study of object detection in arbitrary directions. It is worth noting that some properties are different from horizontal object detection during oriented object detection that researchers have yet to notice much. This article presents the design of a straightforward and efficient arbitrary-oriented detection system, leveraging the inherent properties of the orientation task, including the rotation angle and box aspect ratio. In the detection of low aspect ratio objects, the angle is of little importance to the orientation bounding box, and it is even difficult to define the angle information in extreme categories. Conversely, in the detection of objects with high aspect ratios, the angle information plays a crucial role and can have a decisive impact on the quality of the detection results. By exploiting the aspect ratio of different targets, this letter proposes a ratio-balanced angle loss that allows the model to make a better trade-off between low-aspect ratio objects and high-aspect ratio objects. The rotation angle of each oriented object, which we naturally embed into a two-dimensional Euclidean space for regression, thus avoids an overly redundant design and preserving the topological properties of the circular space. The performance of the UCAS-AOD, HRSC2016, and DLR-3K datasets show that the proposed model in this paper achieves a leading level in terms of both accuracy and speed. Full article

► Show Figures

Figure 1

22 pages, 6992 KiB

Open AccessArticle

AHF: An Automatic and Universal Image Preprocessing Algorithm for Circular-Coded Targets Identification in Close-Range Photogrammetry under Complex Illumination Conditions

by Hang Shang and Changying Liu

Remote Sens. 2023, 15(12), 3151; https://doi.org/10.3390/rs15123151 - 16 Jun 2023

Viewed by 1002

Abstract

In close-range photogrammetry, circular-coded targets (CCTs) are a reliable method to solve the issue of image correspondence. Currently, the identification methods for CCTs are very mature, but complex illumination conditions are still a key factor restricting identification. This article proposes an adaptive homomorphic filtering (AHF) algorithm to solve this issue, utilizing homomorphic filtering (HF) to eliminate the influence of uneven illumination. However, HF parameters vary with different lighting types. We use a genetic algorithm (GA) to carry out global optimization and take the identification result as the objective function to realize automatic parameter adjustment. This is different from the optimization strategy of traditional adaptive image enhancement methods, so the most significant advantage of the proposed algorithm lies in its automation and universality, i.e., users only need to input photos without considering the type of lighting conditions. As a preprocessing algorithm, we conducted experiments combining advanced commercial photogrammetric software and traditional identification methods, respectively. We cast stripe- and lattice-structured light to create complex lighting conditions, including uneven lighting, dense shadow areas, and elliptical light spots. Experiments showed that our algorithm significantly improves the robustness and accuracy of CCT identification methods under complex lighting conditions. Given the perfect performance under stripe-structured light, this algorithm can provide a new idea for the fusion of close-range photogrammetry and structured light. This algorithm helps to improve the quality and accuracy of photogrammetry and even helps to improve the decision making and planning process of photogrammetry. Full article

► Show Figures

Graphical abstract

19 pages, 7823 KiB

Open AccessArticle

Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling

by Takashi Fuse and Kazuki Imose

Remote Sens. 2023, 15(11), 2714; https://doi.org/10.3390/rs15112714 - 23 May 2023

Viewed by 1047

Abstract

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m. Full article

► Show Figures

Figure 1

14 pages, 2493 KiB

Open AccessCommunication

Fighting Illicit Trafficking of Cultural Goods—The ENIGMA Project

by Petros Patias and Charalampos Georgiadis

Remote Sens. 2023, 15(10), 2579; https://doi.org/10.3390/rs15102579 - 15 May 2023

Cited by 2 | Viewed by 1796

Abstract

Cultural heritage is a testimony of past human activity, and, as such, cultural objects exhibit great variety in their nature, size, and complexity, from small artefacts and museum items to cultural landscapes, and from historic buildings and ancient monuments to city centers and archaeological sites. Cultural heritage around the globe suffers from wars, natural disasters, and human negligence. More specifically, cultural goods and artefacts are put at risk through several anthropogenic actions: Anthropogenic threats take various dimensions, ranging from theft from museums, private collections, and religious buildings, smuggling of and illicit trade in cultural goods, the irremediable looting and demolition of archaeological sites by clandestine excavators, or simply neglect of heritage sites. Illicit trading has expanded dramatically recently, especially in areas affected by armed conflicts and natural disasters, either aiming at destroying collective memory and dismembering people’s identity or mostly motivated by the pursuit of profit. Moreover, the illicit trafficking of cultural goods contributes to the funding of terrorism, organized crime, and money laundering. The mission of ENIGMA, a EUR 4 million EU funded project, is to achieve excellence in the protection of cultural goods and artefacts from man-made threats by contributing to their identification, traceability, and provenance research, as well as by safeguarding and monitoring endangered heritage sites. ENIGMA objectives are designed to help the involved stakeholders better respond to this complex and multi-dimensional problem and leverage active collaboration by fostering and enabling interlinking of databases, and evidence-based deployment of preventative measures. Full article

► Show Figures

Figure 1

Review

Jump to: Research

18 pages, 4845 KiB

Open AccessReview

Contribution of Photogrammetry for Geometric Quality Assessment of Satellite Data for Global Climate Monitoring

by Sultan Kocaman and Gabriela Seiz

Remote Sens. 2023, 15(18), 4575; https://doi.org/10.3390/rs15184575 - 17 Sep 2023

Viewed by 1071

Abstract

This article reviews the role that photogrammetry plays in evaluating the geometric quality of satellite products in connection to the long-term monitoring of essential climate variables (ECVs). The Global Climate Observing System (GCOS) is responsible for defining the observations required for climate monitoring. Only satellite products are capable of providing high-quality observations of a particular subset of ECVs on a global scale. Geometric calibration and validation of these products are crucial for ensuring the coherence of data obtained across platforms and sensors and reliable monitoring in the long term. Here, we analyzed the GCOS implementation plan and the data quality requirements and explored various geometric quality aspects, such as internal and external accuracy and band-to-band registration assessment, for a number of satellite sensors commonly used for climate monitoring. Both geostationary (GEO) and low-earth orbit (LEO) sensors with resolutions between 250 m and 3 km were evaluated for this purpose. The article highlights that the geometric quality issues vary with the sensor, and regular monitoring of data quality and tuning of calibration parameters are essential for identifying and reducing the uncertainty in the derived climate observations. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Photogrammetry – the Science of Precise Measurements from Images: A Themed Issue in Honour of Professor Emeritus Armin Grün in Anticipation of His 80th Birthday

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI