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Remote Sensing
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Satellite Image Processing and Analysis
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Satellite Image Processing and Analysis

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 (28 February 2023) | Viewed by 2554

Special Issue Editor

Prof. Dr. Joaquín Alonso-Montesinos

E-Mail Website
Guest Editor
Department of Chemistry and Physics, University of Almería, 04120 Almeria, Spain
Interests: solar irradiance forecasting; cloud forecasting; CSP plants; PV plants; atmospheric extinction; sky cameras; satellite images; remote sensing; artificial neural networks; image processing; cloud detection; solar irradiance estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite images provide an accurate depiction of the earth and its environment in real time. The large constellation of remote sensing satellites orbiting the earth provides comprehensive and periodic coverage of the earth, enabling myriad uses for the benefit of mankind, including crop mapping and monitoring, land cover/land use mapping, change detection, and disaster management. Due to this, nowadays, satellite image processing represents an important area of research due to its wide range of applications.

For now, many methods and algorithms exist, and many can be developed to properly enhance and extract information from satellite images in different ranges of frequencies, recorded at different scales. The objective of the present Special Issue is to cover the relevant topics, trends, and best practices regarding algorithms, models, analysis, and applications of satellite images for remote sensing. We welcome topics that include, but are not limited to, the following:

  • Satellite image processing;
  • Image analysis;
  • Image matching;
  • Deep learning;
  • Change detection and multi-temporal analysis;
  • Data fusion and integration of multi-sensor data;
  • Image segmentation and classification algorithms.

Prof. Dr. Joaquín Alonso-Montesinos
Guest Editor

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

  • satellite image processing
  • satellite image time series
  • deep learning
  • image analysis
  • image matching
  • multi-source image fusion

Published Papers (1 paper)

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Research

18 pages, 16146 KiB  
Article
A Spatiotemporal Atmospheric Refraction Correction Method for Improving the Geolocation Accuracy of High-Resolution Remote Sensing Images
by Xiaohong Peng, Wenwen Huang, Xiaoyan Li, Lin Yang and Fansheng Chen
Remote Sens. 2022, 14(21), 5344; https://doi.org/10.3390/rs14215344 - 25 Oct 2022
Cited by 5 | Viewed by 2204
Abstract
Atmospheric refraction is one of the most significant factors that affect the geolocation accuracy of high-resolution remote sensing images. However, most of the current atmospheric refraction correction methods based on empirical data neglect the spatiotemporal variation of pressure, temperature, and humidity of the [...] Read more.
Atmospheric refraction is one of the most significant factors that affect the geolocation accuracy of high-resolution remote sensing images. However, most of the current atmospheric refraction correction methods based on empirical data neglect the spatiotemporal variation of pressure, temperature, and humidity of the atmosphere, inevitably resulting in poor geometric positioning accuracy. Therefore, in terms of the problems mentioned above, this study proposed a spatiotemporal atmospheric refraction correction method (SARCM) based on global measured data to avoid the uncertainty of traditional empirical models. Initially, the atmosphere was stratified into 42 layers according to their pressure property, and each layer was divided into 1,042,560 grid cells with intervals of 0.25 longitude and 0.25 latitude. Then, the atmospheric refractive index of each grid in the imaging region was accurately calculated using the high-precision Ciddor formula, and the result was interpolated using three splines. Subsequently, according to the rigorous geometric positioning model, the line-of-sight of each pixel and the viewing zenith angle outside the atmosphere in WGS84 were derived to provide input for atmospheric refraction correction. Finally, the coordinates of the ground control points were corrected with the calculated atmospheric refractive index and Snell’s law. The experimental results showed that the proposed SARCM could effectively improve the positioning accuracy of the image with a large viewing zenith angle, and especially, the improvement percentage for a viewing zenith angle of 34.2426° in the x-direction was 99.5%. Moreover, the atmospheric refraction correction result of the SARCM was better than that of the primary state-of-the-art methods. Full article
(This article belongs to the Special Issue Satellite Image Processing and Analysis)
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