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Special Issue "Image Processing and Spatial Neighbourhoods for Remote Sensing Data 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: 20 August 2020

Special Issue Editors

Guest Editor
Dr. Wenzhi Liao

Universiteit Gent, Department of Telecommunications and Information Processing, Ghent, Belgium
Website | E-Mail
Interests: remote sensing; hyperspectral imaging; mathematical morphology; machine learning; multisensor data fusion
Guest Editor
Dr. Pedram Ghamisi

Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Division Exploration, Machine Learning Group, Germany
Website | E-Mail
Phone: +491796931140
Interests: spectral and spatial techniques for hyperspectral image classification; multisensor data fusion; machine learning; deep learning
Guest Editor
Dr. Lianru Gao

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China
Website | E-Mail
Interests: remote sensing; Earth observation; hyperspectral image processing; target detection
Guest Editor
Prof. Jocelyn Chanussot

GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402 SAINT MARTIN D'HERES CEDEX, France
Website | E-Mail
Phone: +33 662 738 444
Interests: image processing; machine learning; mathematical morphology; hyperspectral imaging; data fusion

Special Issue Information

Dear Colleagues,

Recent advances in remote sensing technologies have led to the increased availability of a multitude of satellite and airborne data sources, with increasing spatial, spectral, and temporal resolutions. Additionally, at lower altitudes, airplanes and Unmanned Aerial Vehicles (UAVs) can deliver very high-resolution data from targeted locations. Remote sensing images of very high geometrical resolution can provide a precise and detailed representation of the surveyed scene. Thus, the spatial information contained in these images is fundamental for any application requiring the analysis of images.

In this Special Issue, we welcome methodological contributions in terms of novel spatial information extraction/modeling algorithms as well as their recent applications to relevant scenarios from remote sensing imagery. We invite you to submit the most recent advancements in (but not limited to) the following topics:

  • Mathematical morphology (e.g., morphological filters, attribute filters, etc.) for the analysis of high-resolution remote sensing images;
  • Image operations based on spatial neighbourhoods;
  • Textural, structural, and semantic feature extraction;
  • Operational methods for incorporating spatial information of high-resolution data;
  • Object-based image processing;
  • Semantic understanding and analysis.

Dr. Wenzhi Liao
Dr. Pedram Ghamisi
Dr. Lianru Gao
Prof. Jocelyn Chanussot
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

  • very high resolution
  • remote sensing
  • spatial information extraction
  • mathematical morphology
  • image processing

Published Papers (2 papers)

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Research

Open AccessArticle
Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation
Remote Sens. 2019, 11(10), 1229; https://doi.org/10.3390/rs11101229 (registering DOI)
Received: 11 April 2019 / Revised: 9 May 2019 / Accepted: 16 May 2019 / Published: 23 May 2019
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Abstract
Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we [...] Read more.
Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method. Full article
Figures

Graphical abstract

Open AccessArticle
Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification
Remote Sens. 2019, 11(9), 1114; https://doi.org/10.3390/rs11091114
Received: 15 March 2019 / Revised: 24 April 2019 / Accepted: 5 May 2019 / Published: 9 May 2019
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Abstract
By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to [...] Read more.
By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models. Full article
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