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Photogrammetry and Image Analysis in Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 17598

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Special Issue Editors

Prof. Celestino Ordoñez

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Guest Editor

Universidad de Oviedo, Department of Mining Exploitation and Prospecting, Oviedo, Spain
Interests: laser scanning; photogrammetry; algorithms for data processing; spatial statistics and GIS

Dr. Carlos Cabo

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Guest Editor

Universidad de Oviedo, Grupo de Investigación en Geomática y Computación Gráfica (Geograph), Oviedo, Spain
Interests: laser scanning; photogrammetry; algorithms for image and point cloud processing; modelling with GIS
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Dr. Enoc Sanz-Ablanedo

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Guest Editor

Universidad de León, Grupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Av. de Astorga, sn, 24401 Ponferrada, Spain
Interests: SfM photogrammetry; UAV imagery; hiperspectral analysis

Special Issue Information

Dear Colleagues,

Photogrammmetry and image analysis have been traditionally very important tools for solving problems in Remote Sensing. We are interested in publishing your work on these subjects, and we would like to invite you to submit articles regarding new methods and applications of photogrammetry, computer vision, and image analysis in Remote Sensing.

Some of the topics covered by this Special Issue are advances in algorithms for image processing (SfM, deep learning, image classification and segmentation, etc.), sensors calibration and integration, the application of photogrammetry and image analysis in cartography, environmental protection, climate change and global warming, precision agriculture, and hazard analysis.

Original research articles and articles providing a profound and critical review of the state of current knowledge are welcome. In addition to applications, we are also interested in new instruments, methods, and algorithms for data processing.

Prof. Celestino Ordoñez
Dr. Carlos Cabo
Dr. Enoc Sanz-Ablanedo
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

SfM and MVS algorithms
Supervised and unsupervised classification
Hyperspectral cameras and LiDAR
Sensors fusion
Data analysis
Environmental monitoring
Precision agriculture
Species distribution

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Published Papers (4 papers)

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Research

24 pages, 11270 KiB

Open AccessArticle

A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using Adaptive Image Quality Modification

by Yeonju Choi, Sanghyuck Han and Yongwoo Kim

Remote Sens. 2021, 13(16), 3301; https://doi.org/10.3390/rs13163301 - 20 Aug 2021

Cited by 4 | Viewed by 2393

Abstract

In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods. Full article

(This article belongs to the Special Issue Photogrammetry and Image Analysis in Remote Sensing)

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23 pages, 6851 KiB

Open AccessArticle

An Integrated Solution for 3D Heritage Modeling Based on Videogrammetry and V-SLAM Technology

by Pedro Ortiz-Coder and Alonso Sánchez-Ríos

Remote Sens. 2020, 12(9), 1529; https://doi.org/10.3390/rs12091529 - 11 May 2020

Cited by 15 | Viewed by 4584

Abstract

This paper presents an approach for 3D reconstruction of heritage scenes using a videogrammetric-based device. The system, based on two video cameras with different characteristics, uses a combination of visual simultaneous localization and mapping (SLAM) and photogrammetry technologies. VSLAM, together with a series of filtering algorithms, is used for the optimal selection of images and to guarantee that the user does not lose tracking during data acquisition in real time. The different photogrammetrically adapted tools in this device and for this type of handheld capture are explained. An evaluation of the device is carried out, including comparisons with the Faro Focus X 330 laser scanner, through three case studies in which multiple aspects are analyzed. We demonstrate that the proposed videogrammetric system is 17 times faster in capturing data than the laser scanner and that the post-processing of the system is fully automatic, but takes more time than the laser scanner in post-processing. It can also be seen that the accuracies of both systems and the generated textures are very similar. Our evaluation demonstrates the possibilities of considering the proposed system as a new professional-quality measurement instrument. Full article

(This article belongs to the Special Issue Photogrammetry and Image Analysis in Remote Sensing)

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20 pages, 9465 KiB

Open AccessArticle

Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms

by Pawel Burdziakowski

Remote Sens. 2020, 12(5), 810; https://doi.org/10.3390/rs12050810 - 3 Mar 2020

Cited by 32 | Viewed by 5797

Abstract

Unmanned aerial vehicles (UAVs) have now become very popular in photogrammetric and remote-sensing applications. Every day, these vehicles are used in new applications, new terrains, and new tasks, facing new problems. One of these problems is connected with flight altitude and the determined ground sample distance in a specific area, especially within cities and industrial and construction areas. The problem is that a safe flight altitude and camera parameters do not meet the required or demanded ground sampling distance or the geometrical and texture quality. In the cases where the flight level cannot be reduced and there is no technical ability to change the UAV camera or lens, the author proposes the use of a super-resolution algorithm for enhancing images acquired by UAVs and, consequently, increase the geometrical and interpretation quality of the final photogrammetric product. The main study objective was to utilize super-resolution (SR) algorithms to improve the geometric and interpretative quality of the final photogrammetric product, assess its impact on the accuracy of the photogrammetric processing and on the traditional digital photogrammetry workflow. The research concept assumes a comparative analysis of photogrammetric products obtained on the basis of data collected from small, commercial UAVs and products obtained from the same data but additionally processed by the super-resolution algorithm. As the study concludes, the photogrammetric products that are created as a result of the algorithms’ operation on high-altitude images show a comparable quality to the reference products from low altitudes and, in some cases, even improve their quality. Full article

(This article belongs to the Special Issue Photogrammetry and Image Analysis in Remote Sensing)

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23 pages, 6274 KiB

Open AccessArticle

Matching Confidence Constrained Bundle Adjustment for Multi-View High-Resolution Satellite Images

by Xiao Ling, Xu Huang, Yongjun Zhang and Gang Zhou

Remote Sens. 2020, 12(1), 20; https://doi.org/10.3390/rs12010020 - 18 Dec 2019

Cited by 8 | Viewed by 3504

Abstract

Bundle adjustment of multi-view satellite images is a powerful tool to align the orientations of all the images in a unified framework. However, the traditional bundle adjustment process faces a problem in detecting mismatches and evaluating low/medium/high-accuracy matches, which limits the final bundle adjustment accuracy, especially when the mismatches are several times more than the correct matches. To achieve more accurate bundle adjustment results, this paper formulates the prior knowledge of matching accuracy as matching confidences and proposes a matching confidence based bundle adjustment method. The core algorithm firstly selects several highest-confidence matches to initially correct orientations of all images, then detects and eliminates the mismatches under the initial orientation guesses and finally formulates both the matching confidences and the forward-backward projection errors as weights in an iterative bundle adjustment process for more accurate orientation results. We compared our proposed method with the famous RANSAC strategy as well as a state-of-the-art bundle adjustment method on the high-resolution multi-view satellite images. The experimental comparisons are evaluated by image checking points and ground control points, which shows that our proposed method is able to obtain more robust and more accurate mismatch detection results than the RANSAC strategy, even though the mismatches are four times more than the correct matches and it can also achieve more accurate orientation results than the state-of-the-art bundle adjustment method. Full article

(This article belongs to the Special Issue Photogrammetry and Image Analysis in Remote Sensing)

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