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
Deep Neural Networks for Hyperspectral Remote Sensing Image Processing (Second...
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

Deep Neural Networks for Hyperspectral Remote Sensing Image Processing (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 1576

Special Issue Editors

Dr. Yulei Wang

E-Mail Website
Guest Editor
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: hyperspectral image processing; multi-source remote sensing image fusion; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Enyu Zhao

E-Mail Website
Guest Editor
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: thermal infrared; hyperspectral; quantitative remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weiying Xie

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: hyperspectral anomaly detection; network compression; efficient distributed learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chein-I Chang

E-Mail Website
Guest Editor
Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Interests: hyperspectral/multispectral image processing; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of our previous Special Issue, titled “Deep Neural Networks for Hyperspectral Remote Sensing Image Processing”, we are happy to announce our next Special Issue.

A hyperspectral image (HSI) is a three-dimensional cube containing rich spatial and spectral information with hundreds of narrow and contiguous wavebands generated by an imaging spectrometer. Each pixel in hyperspectral remote sensing images corresponds to a nearly continuous spectral curve, which can reflect substances' diagnostic spectral absorption differences and provide rich spectral information for an accurate extraction of ground object information. Thanks to its high spectral resolution, hyperspectral images have received reasonable attention and have essential applications in military and civil fields. In recent years, with the continuous improvement of the hyperspectral data acquisition capability of satellites and aerial platforms, hyperspectral image processing has also developed towards big data-driven feature information extraction. However, processing the massive data collected by these platforms using traditional image analysis methodologies is impractical and ineffective. This calls for the adoption of powerful techniques that can extract reliable and useful information, where deep neural networks have been gradually applied in HSI processing due to the strong generalization and deep extraction properties of advanced semantic features.

This Special Issue aims to explore features that truly benefit hyperspectral remote sensing interpretation tasks and provides a forum for many individuals working in deep-learning-based hyperspectral image processing to report their research findings and share their experiences with the HSI community. All contributions regarding deep neural networks for hyperspectral remote sensing image processing are welcome to this Special Issue. Topics of interest include, but are not limited to, the following:

  • Deep neural networks for target detection, band selection, and classification in hyperspectral images.
  • Deep learning for surface parameters retrieval from thermal infrared images.
  • Deep feature extraction for multi-source remote sensing images.
  • The hybrid architecture of CNN and transformer for hyperspectral applications.
  • Feature fusion and learning for hyperspectral image processing.
  • Lightweight design of deep models.
  • Reviews/surveys regarding recent applications and techniques of hyperspectral images.

We look forward to your submissions.

Dr. Yulei Wang
Dr. Enyu Zhao
Prof. Dr. Weiying Xie
Prof. Dr. Chein-I Chang
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

  • deep neural networks
  • deep feature extraction
  • lightweight model
  • hyperspectral remote sensing images
  • thermal infrared
  • quantitative remote sensing
  • multi-sensor and multi-platform analyses
  • remote sensing applications

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

24 pages, 15151 KB  
Article
SG-YOLO: A Multispectral Small-Object Detector for UAV Imagery Based on YOLO
by Binjie Zhang, Lin Wang, Quanwei Yao, Keyang Li and Qinyan Tan
Remote Sens. 2026, 18(7), 1003; https://doi.org/10.3390/rs18071003 - 27 Mar 2026
Viewed by 521
Abstract
Object detection in unmanned aerial vehicle (UAV) imagery remains a crucial yet challenging task due to complex backgrounds, large scale variations, and the prevalence of small objects. Visible-spectrum images lack robustness under all-weather and all-illumination conditions; by contrast, multispectral sensing provides complementary cues [...] Read more.
Object detection in unmanned aerial vehicle (UAV) imagery remains a crucial yet challenging task due to complex backgrounds, large scale variations, and the prevalence of small objects. Visible-spectrum images lack robustness under all-weather and all-illumination conditions; by contrast, multispectral sensing provides complementary cues (e.g., thermal signatures) that improve detection robustness. However, existing multispectral solutions often incur high computational costs and are therefore difficult to deploy on resource-constrained UAV platforms. To address these issues, SG-YOLO is proposed, a lightweight and efficient multispectral object detection framework that aims to balance accuracy and efficiency. First, a Spectral Gated Downsampling Stem (SGDS) is designed, in which grouped convolutions and a gating mechanism are employed at the early stage of the network to extract band-specific features, thereby maximizing spectral complementarity while minimizing redundancy. Second, a Spectral–Spatial Iterative Attention Fusion (SSIAF) module is introduced, in which spectral-wise (channel) attention and spatial-wise attention are iteratively coupled and cascaded in a multi-scale manner to jointly model cross-band dependencies and spatial saliency, thereby aggregating high-level semantic information while suppressing redundant spectral responses. Finally, a Spatial–Channel Synergistic Fusion (SCSF) module is designed to enhance multi-scale and cross-channel feature integration in the neck. Experiments on the MODA dataset show that SG-YOLOs achieves 72.4% mAP50, outperforming the baseline by 3.2%. Moreover, compared with a range of mainstream one-stage detectors and multispectral detection methods, SG-YOLO delivers the best overall performance, providing an effective solution for UAV object detection while maintaining a favorable trade-off between model size and detection accuracy. Full article
(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing (Second Edition))
►▼ Show Figures

Figure 1

20 pages, 6717 KB  
Article
Unraveling Patch Size Effects in Vision Transformers: Adversarial Robustness in Hyperspectral Image Classification
by Shashi Kiran Chandrappa, Sidike Paheding and Abel A. Reyes-Angulo
Remote Sens. 2026, 18(4), 656; https://doi.org/10.3390/rs18040656 - 21 Feb 2026
Viewed by 492
Abstract
Vision Transformers (ViTs) have demonstrated strong performance in hyperspectral image (HSI) classification; however, their robustness is highly sensitive to patch size. This study investigates the impact of spatial patch size on clean accuracy and adversarial robustness using a standard ViT and a Channel [...] Read more.
Vision Transformers (ViTs) have demonstrated strong performance in hyperspectral image (HSI) classification; however, their robustness is highly sensitive to patch size. This study investigates the impact of spatial patch size on clean accuracy and adversarial robustness using a standard ViT and a Channel Attention Fusion variant (ViT-CAF). Patch sizes from 1 × 1 to 19 × 19 are evaluated across four benchmark datasets under FGSM, BIM, CW, PGD, and RFGSM attacks. Descriptive results show that smaller patches, particularly 1 × 1 and 3 × 3, generally yield higher adversarial accuracy, while larger patches amplify localized perturbations and degrade robustness. Parameter analysis indicates that patch-size-dependent variations arise mainly from the embedding layer, with the Transformer backbone remaining fixed, confirming that robustness differences are driven primarily by spatial context rather than model capacity. These findings reveal a trade-off between spatial granularity and adversarial resilience and provide guidance for patch size selection in ViT-based HSI applications. Full article
(This article belongs to the Special Issue Deep Neural Networks for Hyperspectral Remote Sensing Image Processing (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