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Remote Sensing
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Feature Papers for Remote Sensing Image Processing Section
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Feature Papers for Remote Sensing Image Processing Section

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 19994

Special Issue Editors

Prof. Dr. Jon Atli Benediktsson

grade E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, University of Iceland, IS 102 Reykjavík, Iceland
Interests: remote sensing; image analysis; pattern recognition; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Riccardo Roncella

E-Mail Website
Guest Editor
Department of Engineering and Architecture, Università degli Studi di Parma, Parco Area delle Scienze, 181/a, 43124 Parma, PR, Italy
Interests: image matching; image orientation; satellite/airborne/UAV photogrammetry; 3D reconstruction; monitoring; laser scanning; vision metrology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedro Melo-Pinto

E-Mail Website
Guest Editor
1. CITAB, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
2. Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal
Interests: computer vision; machine learning; hyperspectral imaging; image classification; object detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The “Feature Paper Special Issue for Remote Sensing Image Processing Section” is from the Remote Sensing Journal (ISSN 2072-4292), and is dedicated to the publication and discussion of research articles, letters, reviews, and communications on all aspects of remote sensing image processing science and technologies.

We welcome reviews and outstanding articles to this Special Issue in order to improve the current knowledge on remote sensing image processing. All submissions for this important Special Issue will be rigorously reviewed according to the Remote Sensing journal guidelines and will be accepted by the editorial office, the editor-in-chief, and editorial board members by invitation only.

Remote Sensing Image Processing Section

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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

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22 pages, 5727 KiB  
Article
A Spliced Satellite Optical Camera Geometric Calibration Method Based on Inter-Chip Geometry Constraints
by Tao Wang, Yan Zhang, Yongsheng Zhang, Zhenchao Zhang, Xiongwu Xiao, Ying Yu and Longhui Wang
Remote Sens. 2021, 13(14), 2832; https://doi.org/10.3390/rs13142832 - 19 Jul 2021
Cited by 10 | Viewed by 2826
Abstract
When in orbit, spliced satellite optical cameras are affected by various factors that degrade the actual image stitching precision and the accuracy of their data products. This is a major bottleneck in the current remote sensing technology. Previous geometric calibration research has mostly [...] Read more.
When in orbit, spliced satellite optical cameras are affected by various factors that degrade the actual image stitching precision and the accuracy of their data products. This is a major bottleneck in the current remote sensing technology. Previous geometric calibration research has mostly focused on stitched satellite images and has largely ignored the inter-chip relationship among original multi-chip images, resulting in accuracy loss in geometric calibration and subsequent image products. Therefore, in this paper, a novel geometric calibration method is proposed for spliced satellite optical cameras. The integral geometric calibration model was developed on inter-chip geometry constraints among multi-chip images, including the corresponding external and internal calibration models. The proposed approach improves uncontrolled geopositioning accuracy and enhances mosaic precision at the same time. For evaluation, images from the optical butting satellite ZiYuan-3 (ZY-3) and mechanical interleaving satellite Tianhui-1 (TH-1) were used for the experiments. Multiple sets of satellite data of the Songshan Calibration field and other regions were used to evaluate the reliability, stability, and applicability of the calibration parameters. The experiment results found that the proposed method obtains reliable camera alignment angles and interior calibration parameters and generates high-precision seamless mosaic images. The calibration scheme is not only suitable for mechanical interleaving cameras with large geometric displacement among multi-chip images but is also effective for optical butting cameras with minor chip offset. It also significantly improves uncontrolled geopositioning accuracy for both types of spliced satellite images. Moreover, the proposed calibration procedure results in multi-chip satellite images being seamlessly stitched together and mosaic errors within one pixel. Full article
(This article belongs to the Special Issue Feature Papers for Remote Sensing Image Processing Section)
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Review

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21 pages, 4414 KiB  
Review
Progress and Trends in the Application of Google Earth and Google Earth Engine
by Qiang Zhao, Le Yu, Xuecao Li, Dailiang Peng, Yongguang Zhang and Peng Gong
Remote Sens. 2021, 13(18), 3778; https://doi.org/10.3390/rs13183778 - 21 Sep 2021
Cited by 119 | Viewed by 15814
Abstract
Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 [...] Read more.
Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective. Full article
(This article belongs to the Special Issue Feature Papers for Remote Sensing Image Processing Section)
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