Special Issue "Signal and Image Processing for Remote Sensing"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Costas Panagiotakis
Website
Guest Editor
Associate Professor, Department of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, 72100, Crete, Greece
Interests: signal processing; image and video analysis; multimedia and pattern recognition
Dr. Eleni Kokinou
Website
Guest Editor
Associate Professor, Department of Agriculture, Hellenic Mediterranean University, Heraklion, 71410, Crete, Greece
Interests: geophysics; geological and environmental studies; data processing and interpretation; geo-modelling and geo-signal processing; geomorphology
Dr. Konstantinos Karantzalos
Website
Guest Editor
Associate Professor, Remote Sensing Laboratory, National Technical University of Athens, 15780, Greece
Interests: hyperspectral imaging; UAVs; earth observation; data fusion; machine learning; computer vision; crop type classification; precision agriculture
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade access to earth observation data from aerial and satellite platforms have been significantly leveraged allowing the unprecedented monitoring of land and marine environments. Remote Sensing data can be multidimensional signals, multispectral, hyperspectral images, radar data, time series and video sequences. Efficient data analytics in these signals is crucial in order to exploit all historical archives as well as the newly acquired observations, maybe also in near real-time. Fusion with GNSS signals, proximate remote sensing observations is also currently challenging. The applications are vast including numerous environmental monitoring tasks, agriculture, safety, security, engineering, etc fields. This Special Issues focuses on Signal and Image Processing for Remote Sensing and willing to explore and highlight the most recent cutting-edge data fusion and analytics in remote sensing.

In particular, several challenges and open problems still waiting for efficient solutions and novel methodologies via signal and image processing techniques. The main goal of this special issue is to address advanced topics related to signal processing, image processing and analysis, pattern recognition and machine learning for remote sensing.

We would like to invite you to submit research and review articles related to your research with respect to the following topics:

  • Analysis of multispectral and hyperspectral data
  • Analysis of SAR and LIDAR signals
  • Analysis of hydroacoustic, seismic and microwave signals
  • Analysis of meteorological and GNSS data
  • Data fusion techniques
  • Classification of remote sensing data
  • Pattern recognition for remote sensing
  • Image segmentation, enhancement and restoration
  • Object detection and recognition
  • Machine learning and deep learning
  • Filtering and Multiresolution Processing
  • Change detection and analysis of time series
  • Extraction of geometric and semantic information from SAR
  • Satellite, airborne, UAV and proximate remote sensing
  • Remote sensing applications
Dr. Costas Panagiotakis
Dr. Eleni Kokinou
Dr. Konstantinos Karantzalos
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 2200 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

  • Remote Sensing
  • Signal and Image Processing
  • Image segmentation
  • Machine learning
  • Pattern recognition

Published Papers (1 paper)

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Research

Open AccessArticle
Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data
Remote Sens. 2020, 12(13), 2087; https://doi.org/10.3390/rs12132087 - 29 Jun 2020
Abstract
Assessing chlorophyll-a (Chl-a) pigments in complex inland water systems is of key importance as this parameter constitutes a major ecosystem integrity indicator. In this study, a methodological framework is proposed for quantifying Chl-a pigments using Earth observation (EO) data [...] Read more.
Assessing chlorophyll-a (Chl-a) pigments in complex inland water systems is of key importance as this parameter constitutes a major ecosystem integrity indicator. In this study, a methodological framework is proposed for quantifying Chl-a pigments using Earth observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and 8 Operational Land Imager (OLI) sensors. The first step of the methodology involves the implementation of stepwise multiple regression (MLR) analysis of the available Chl-a dataset. Then, principal component analysis (PCA) is performed to explore Greek lakes’ potential interrelationships based on their Chl-a values in conjunction with certain criteria: their characteristics (artificial/natural), typology, and climatic type. Additionally, parameters such as seasonal water sampling and the date difference between sampling and satellite overpass are taken into consideration. Next, is implemented a stepwise multiple regression analysis among different groups of cases, formed by the criteria indicated from the PCA itself. This effort aimed at exploring different remote sensing-derived Chl-a algorithms for various types of lakes. The practical use of the proposed approach was evaluated in a total of 50 lake water bodies (natural and artificial) from 2013–2018, constituting the National Lake Network Monitoring of Greece in the context of the Water Framework Directive (WFD). All in all, the results evidenced the suitability of Landsat data when used with the proposed technique to estimate log-transformed Chl-a. The proposed scheme resulted in the development of models separately for natural (R = 0.78; RMSE = 1.3 μg/L) and artificial lakes (R = 0.76; RMSE = 1.29 μg/L), while the model developed without criteria proved weaker (R = 0.65; RMSE = 1.85 μg/L) in comparison to the other ones examined. The methodological framework proposed herein can be used as a useful resource toward a continuous monitoring and assessment of lake water quality, supporting sustainable water resources management. Full article
(This article belongs to the Special Issue Signal and Image Processing for Remote Sensing)
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