Special Issue "New Advances on Sub-pixel Processing: Unmixing and Mapping Methods"

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

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Dr. Addisson Salazar
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Guest Editor
Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain
Tel. +34 963877930
Interests: pattern recognition; signal processing on graphs; dynamic modeling; decision fusion; machine learning
Special Issues and Collections in MDPI journals
Prof. Luis Vergara
E-Mail Website
Guest Editor
Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain
Tel. +34 963877308
Interests: statistical signal processing; statistical signal processing; pattern recognition; machine learning; graph signal processing
Special Issues and Collections in MDPI journals
Dr. Gonzalo Safont
E-Mail Website
Guest Editor
Universitat Politècnica de València, Institute of Telecommunications and Multimedia Applications 46022 Valencia, Spain
Tel. +34 963879985
Interests: Independent Component Analysis, Signal Processing, Pattern Recognition, Classification Methods, Image Processing, Decision Fusion, Biosignals, Remote Sensing

Special Issue Information

Dear Colleagues,

Sub-pixel processing takes into account the possibility of a pixel to belong to different classes in an image segmentation context. This is especially relevant in remote sensing, where different macroscopic or microscopic components could appear to contribute to every pixel. Thus sub-pixel segmentation can increase the resolution of the original images, which is limited by the sensorial systems and several measurement effects. Two main problems are identified in sub-pixel processing: unmixing and mapping. Unmixing refers to the separation of the components contributing to the pixel information (typically a pixel signature obtained from hyperspectral images) to build abundance maps describing the proportion of every component in a given pixel. Mapping is the distribution inside a pixel of its labelled sub-pixels, consistently with the abundance maps.

A good variety of options have been proposed so far, but there is still room for new advances in this challenging problem. Thus nonlinear decomposition techniques like Independent Component Analysis, Bounded Component Analysis or Nonnegative Matrix Factorization, among others, are candidates to improve conventional unmixing methods based on linear models like Principal Component Analysis. On the other hand, sub-pixel mapping is an ill-conditioned problem as far as many possible sub-pixel distributions are compatible with the estimated abundance maps. Advanced solutions from machine learning and pattern analysis and recognition have been also devised to solve the inherent problems of sub-pixel processing. This is symptomatic of the very diverse approaches adopted and highlights the potential of research in this area.

Thus the aim of this special issue is to contribute with new theoretical and practical insights in this booming topic focused to the remote sensing imagery.

Dr. Addisson Salazar
Prof. Luis Vergara
Dr. Gonzalo Safont
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.

Keywords

  • Pixel swapping model (PSM)
  • Spatial attraction model (SAM)
  • Spatial correlation
  • Sub-pixel mapping (SPM)
  • Hyper spectral images
  • Artificial neural networks
  • Deep Learning
  • Conventional Neural Networks
  • Image interpolation
  • Image classification
  • Sub-pixel change detection
  • Pattern analysis and recognition
  • Classification methods
  • Independent component analysis
  • Principal component analysis
  • Bounded component analysis

Published Papers (1 paper)

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Research

Open AccessArticle
Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network
Remote Sens. 2019, 11(15), 1815; https://doi.org/10.3390/rs11151815 - 02 Aug 2019
Abstract
Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown [...] Read more.
Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN ( SRM CNN ) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRM CNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRM CNN method was validated by visualizing output features and analyzing the performance of different geographic objects. Full article
(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)
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