Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Advances in Remote Sensing of Hyperspectral Image Processing and Radiative...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Remote Sensing of Hyperspectral Image Processing and Radiative Transfer Modeling

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 (30 November 2023) | Viewed by 6444

Special Issue Editors

Dr. Melina Maria Zempila

E-Mail Website
Guest Editor
RAL Space, Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 0QX, UK
Interests: hyperspectral imagery applications; calibration; radiative transfer
Dr. Yameng Cao

E-Mail Website
Guest Editor
National Physical Laboratory, Teddington, Middlesex, UK
Interests: hyperspectral Imaging; application of optical spectroscopic techniques (e.g., Raman, PL, imaging) and kinematic modelling of materials

Special Issue Information

Dear Colleagues,

Hyperspectral image (HSI) processing is one of the cutting-edge fields in remote sensing (RS), with many applications in precision agriculture, Earth observation, environmental monitoring, resource exploration and many more. As the demand for robust and precise HSI processing tools and techniques has been increasing in recent years, a wide number of studies have introduced machine learning (ML) techniques alongside chemometrics and pre-processing techniques, multivariate statistical data analysis tools and radiative transfer modeling to improve HSI quality and advance feature extraction and classification for both in-lab and/or field applications.

This Special Issue aims to compile studies covering different aspects of HSI processing algorithms and workflows that can enhance the spatial and/or spectral information contained across the HSI, and hence studies discussion pre-processing, calibration (geometric and/or radiometric) and radiative transfer modeling techniques are welcome. Newly proposed object/pattern recognition and classification algorithms, along with advanced ML and artificial intelligence (AI) approaches, can also be hosted in this Special Issue.

Articles may address, but are not limited to, the following HSI-related topics:

  • Precision agriculture
  • Earth observation
  • Environmental monitoring
  • Resource exploration
  • Data fusion
  • Drone photogrammetry
  • Soil research
  • Water research

Dr. Melina Maria Zempila
Dr. Yameng Cao
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 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

  • HSI spatial and spectral pre-processing
  • HSI processing
  • HSI calibration
  • radiative transfer for HSI RS
  • dimensionality reduction in HSI
  • HSI band selection
  • classification in HSI
  • HSI applications
  • machine learning in HSI
  • HSI data fusion

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 3188 KiB  
Article
Identifying Core Wavelengths of Oil Tree’s Hyperspectral Data by Taylor Expansion
by Zhibin Sun, Xinyue Jiang, Xuehai Tang, Lipeng Yan, Fan Kuang, Xiaozhou Li, Min Dou, Bin Wang and Xiang Gao
Remote Sens. 2023, 15(12), 3137; https://doi.org/10.3390/rs15123137 - 15 Jun 2023
Viewed by 1153
Abstract
The interference of background noise leads to the extremely high spatial complexity of hyperspectral data. Sensitive band selecting is an important task to minimize or eliminate the influence of non-target elements. In this study, Taylor expansion is innovatively used to identify core wavelengths/bands [...] Read more.
The interference of background noise leads to the extremely high spatial complexity of hyperspectral data. Sensitive band selecting is an important task to minimize or eliminate the influence of non-target elements. In this study, Taylor expansion is innovatively used to identify core wavelengths/bands of hyperspectral data. Unlike other traditional methods, this proposed Taylor-CC method considers more local and global information of spectral function to estimate the linear/nonlinear correlation between two wavelengths. Using samples of hyperspectral data with a wavelength range of 350–2500 nm and SPAD for Camellia oleifera, this Taylor-CC method is compared with the traditional PCC method derived from the Pearson correlation coefficient. Using the 240 samples with their different 57 core wavelengths identified by the Taylor-CC method and PCC method, three machine models (i.e., random forest-RF, linear regression-LR, and artificial neural network-ANN) are trained to compare their performances. Their results show that the correlation matrix from the Taylor-CC method represents a clear diagonal pattern with near zero values at most locations away from the diagonal, and all three models confirm that the Taylor-CC method is superior to the PCC method. Moreover, the SPAD spectral response relationship based on machine learning algorithms is constructed, and ANN is the best prediction performance among the three models when using the core wavelengths identified by the Taylor-CC method. The Taylor-CC method proposed in this study not only lays a mathematical foundation for the next analysis of the response mechanism between spectral characteristics and nutrient content of Camellia leaf, but also provides a new idea for the correlation analysis of adjacent spectral bands for hyperspectral signals in many applications. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Hyperspectral Image Processing and Radiative Transfer Modeling)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

36 pages, 14055 KiB  
Review
A Review of Hyperspectral Image Super-Resolution Based on Deep Learning
by Chi Chen, Yongcheng Wang, Ning Zhang, Yuxi Zhang and Zhikang Zhao
Remote Sens. 2023, 15(11), 2853; https://doi.org/10.3390/rs15112853 - 31 May 2023
Cited by 11 | Viewed by 4769
Abstract
Hyperspectral image (HSI) super-resolution (SR) is a classical computer vision task that aims to accomplish the conversion of images from lower to higher resolutions. With the booming development of deep learning (DL) technology, more and more researchers are dedicated to the research of [...] Read more.
Hyperspectral image (HSI) super-resolution (SR) is a classical computer vision task that aims to accomplish the conversion of images from lower to higher resolutions. With the booming development of deep learning (DL) technology, more and more researchers are dedicated to the research of image SR techniques based on DL and have made remarkable progress. However, no scholar has provided a comprehensive review of the field. As a response, in this paper we aim to supply a comprehensive summary of the DL-based SR techniques for HSI, including upsampling frameworks, upsampling methods, network design, loss functions, representative works with different strategies, and future directions, in which we design several sets of comparative experiments for the advantages and limitations of two-dimensional convolution and three-dimensional convolution in the field of HSI SR and analyze the experimental results in depth. In addition, the paper also briefly discusses the secondary foci such as common datasets, evaluation metrics, and traditional SR algorithms. To the best of our knowledge, this paper is the first review on DL-based HSI SR. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Hyperspectral Image Processing and Radiative Transfer Modeling)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop