Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
Recent Advances in the Processing of Hyperspectral Images (Second Edition)
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances in the Processing of Hyperspectral Images (Second Edition)

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 May 2026 | Viewed by 2792

Special Issue Editors

Prof. Dr. Liguo Wang

E-Mail Website
Guest Editor
College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China
Interests: remote sensing image processing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yanfeng Gu

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: space intelligent remote sensing; multi-mode hyperspectral remote sensing; intelligent application of remote sensing big data
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Wang

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
Interests: remote sensing imagery processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Henry Leung

E-Mail Website
Guest Editor
Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent sensors; machine learning; data analytics; information fusion; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We extend our thanks for all the effort and support received in successfully producing our previous Special Issue: ‘Recent Advances in the Processing of Hyperspectral Images (https://www.mdpi.com/journal/remotesensing/special_issues/RQQO435VOB). Submissions are now open for a new edition of this Special Issue, as detailed below.

Hyperspectral imagery (HSI) has become one of the most important tools for acquiring data to analyze the monitoring and evaluation of resources and the ecological environment. However, due to the limitations of sensors and the complexity of resources in the ecological environment, there are often many mixed pixels in the obtained HSI, which brings great challenges to the mapping of resource ecological environments. Therefore, one of the most significant spectral issues in remote sensing research is how to process mixed pixels for HSI to obtain more accurate resource ecological environment mapping information. Many hyperspectral image processing techniques are developing rapidly to process mixed pixels. In particular, the development of computer technology and calculation techniques such as artificial intelligence, deep learning, and weakly supervised learning has expanded the application and scope of hyperspectral image processing in recent years. However, several challenges still require efficient solutions and novel methodologies. The main goal of this Special Issue is to address advanced topics related to hyperspectral image processing.

  • Fusion and resolution enhancement;
  • Denoising, restoration, and super-resolution;
  • Endmember extraction and unmixing;
  • Dimensionality reduction and band selection;
  • Classification and segmentation;
  • Subpixel mapping;
  • Change detection and time-series HSI analysis;
  • Artificial intelligence for HSI;
  • Deep learning for HSI.

Prof. Dr. Liguo Wang
Prof. Dr. Yanfeng Gu
Dr. Peng Wang
Prof. Dr. Henry Leung
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 250 words) can be sent to the Editorial Office for assessment.

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

  • remote sensing image processing
  • hyperspectral image
  • mixed pixels
  • machine learning
  • deep learning

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

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

21 pages, 6097 KB  
Article
HySIMU: An Open-Source Toolkit for Hyperspectral Remote Sensing Forward Modelling
by Fadhli Atarita and Alexander Braun
Remote Sens. 2026, 18(6), 943; https://doi.org/10.3390/rs18060943 - 20 Mar 2026
Viewed by 238
Abstract
Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions [...] Read more.
Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions as well as an increase in the availability of commercial sensors and platforms, such as drones. A significant challenge in deploying the varied platforms and sensors is the design and optimization of the hyperspectral surveys. Forward modelling simulators are valuable for optimizing mission parameters and estimating imaging performance. Limited accessibility of open-source simulators presents an obstacle for users who seek to benefit from such tools. To bridge this gap, HySIMU (Hyperspectral SIMUlator) was developed and described herein. It is an open-source, forward modelling toolkit that combines and integrates a primary processing pipeline with various open-source packages into a transparent and modular workflow. It offers a cost-effective approach to evaluating the performance of hyperspectral surveys. HySIMU is designed to simulate hyperspectral imagery based on user-defined targets, platforms, and sensor parameters. Features include (i) a ground truth data cube builder for customizable input parameters, (ii) a terrain-based solar and view geometry calculator for illumination modelling, (iii) integrated open-source radiative transfer models for incorporating atmospheric effects, and (iv) spatial resampling filters. In this manuscript, the initial framework for HySIMU is presented with some example applications, including two validation studies with real hyperspectral images. As remote sensing technologies advance, forward modelling toolkits such as HySIMU play a crucial role in refining mission designs and assessing survey feasibility. The scalability for arbitrary hyperspectral sensors, platforms, and spectral libraries ensures broad applicability. Of particular importance is support for parameter optimization for both scientific and commercial HRS campaigns. Full article
(This article belongs to the Special Issue Recent Advances in the Processing of Hyperspectral Images (Second Edition))
►▼ Show Figures

Figure 1

25 pages, 5445 KB  
Article
HyperspectralMamba: A Novel State Space Model Architecture for Hyperspectral Image Classification
by Jianshang Liao and Liguo Wang
Remote Sens. 2025, 17(15), 2577; https://doi.org/10.3390/rs17152577 - 24 Jul 2025
Cited by 7 | Viewed by 1926
Abstract
Hyperspectral image classification faces challenges with high-dimensional spectral data and complex dependencies between bands. This paper proposes HyperspectralMamba, a novel architecture for hyperspectral image classification that integrates state space modeling with adaptive recalibration mechanisms. The method addresses limitations in existing techniques through three [...] Read more.
Hyperspectral image classification faces challenges with high-dimensional spectral data and complex dependencies between bands. This paper proposes HyperspectralMamba, a novel architecture for hyperspectral image classification that integrates state space modeling with adaptive recalibration mechanisms. The method addresses limitations in existing techniques through three key innovations: (1) a novel dual-stream architecture that combines SSM global modeling with parallel convolutional local feature extraction, distinguishing our approach from existing single-stream SSM methods; (2) a band-adaptive feature recalibration mechanism specifically designed for hyperspectral data that adaptively adjusts the importance of different spectral band features; and (3) an effective feature fusion strategy that integrates global and local features through residual connections. Experimental results on three benchmark datasets—Indian Pines, Pavia University, and Salinas Valley—demonstrate that the proposed method achieves overall accuracies of 95.31%, 98.60%, and 96.40%, respectively, significantly outperforming existing convolutional neural networks, attention-enhanced networks, and Transformer methods. HyperspectralMamba demonstrates an exceptional performance in small-sample class recognition and distinguishing spectrally similar terrain, while maintaining lower computational complexity, providing a new technical approach for high-precision hyperspectral image classification. Full article
(This article belongs to the Special Issue Recent Advances in the Processing of Hyperspectral Images (Second Edition))
►▼ 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