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Special Issue "Advanced Topics in Remote Sensing"

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

Deadline for manuscript submissions: 20 February 2019

Special Issue Editors

Guest Editor
Dr. Xiaofeng Li

NCWCP - E/RA3, 5830 University Research Court, College Park, MD 20740, U.S.A.
Website | E-Mail
Phone: (301)683-3314
Fax: (301)683-3301
Interests: AI oceanography, big data, ocean remote sensing, physical oceanography, boundary layer meteorology, synthetic aperture radar imaging mechanism, multiple-polarization radar applications, satellite image classification and segmentation
Guest Editor
Dr. Yanfei Zhong

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University; 129 Luoyu Road, Wuhan 430079, China
Website | E-Mail
Interests: multi- and hyperspectral remote sensing data processing; high resolution image processing and scene analysis; and computational intelligence

Special Issue Information

Dear Colleagues,

The 2018 International Conference on Advanced Remote Sensing will be held in Wuhan, China, 16–18 October 2018. Remote Sensing (IF: 3.406) will launch a Special Issue, entitled “Advanced Topics in Remote Sensing”, following this conference. The Special Issue will consist of papers presented at this conference in the following areas:

1. remote sensing sensors and data acquisition

satellite data acquisition, airborne data acquisition, ground-based data acquisition

2. remote sensing data processing

high-resolution image processing, hyperspectral image processing, synthetic aperture radar data processing, visible and infrared image processing, laser scanning data processing

3. remote sensing application

land, atmosphere, ocean, and cryosphere applications

All submitted manuscripts will be peer reviewed according to Remote Sensing guidelines, and they should not have been published or be considered for publication elsewhere. The publisher, MDPI, offers conference participants a 20% discount on the publishing fees. Papers will be published online within a week after the acceptance.

For questions and inquiries, please contact the Assistant Editor:

Ms. Yanhua Li at [email protected]

Dr. Xiaofeng Li
Dr. Yanfei Zhong
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 1800 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.

Published Papers (2 papers)

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Research

Open AccessArticle Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China
Remote Sens. 2018, 10(12), 2045; https://doi.org/10.3390/rs10122045
Received: 14 September 2018 / Revised: 9 December 2018 / Accepted: 14 December 2018 / Published: 16 December 2018
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Abstract
This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies
[...] Read more.
This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies with identical root mean square errors (RMSE). Nevertheless, MuSyQ albedo is better correlated with ground measurements when sufficient valid observations are available or snow-free. The opposite happens when less than seven valid observations are available. GLASS albedo presents a larger RMSE than MCD43A3 and MuSyQ albedos in comparison with ground measurements. Over surfaces with smaller seasonal variations, MCD43A3 and MuSyQ albedos show smaller RMSEs than GLASS albedo in comparison with HJ albedo. However, for surfaces with larger temporal variations, both RMSEs and R2 of GLASS albedo are comparable with MCD43A3 and MuSyQ. Generally, MCD43A3 and MuSyQ albedos featured the same RMSEs of 0.034 and similar R2 (0.920 and 0.903, respectively), which are better than GLASS albedo (RMSE = 0.043, R2 = 0.787). However, when it comes to comparison with aggregated HJ albedo, MuSyQ and GLASS albedos are with lower RMSEs of 0.027 and 0.032 and higher R2 of 0.900 and 0.898 respectively than MCD43A3 (RMSE = 0.038, R2 = 0.836). Despite the limited geographic region of the study area, they still provide an important insight into the accuracies of three albedo products. Full article
(This article belongs to the Special Issue Advanced Topics in Remote Sensing)
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Open AccessArticle An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images
Remote Sens. 2018, 10(12), 1863; https://doi.org/10.3390/rs10121863
Received: 14 September 2018 / Revised: 19 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
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Abstract
Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting
[...] Read more.
Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy. Full article
(This article belongs to the Special Issue Advanced Topics in Remote Sensing)
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Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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