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
Artificial Intelligence and Machine Learning with Applications in Remote Sensing...
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

Artificial Intelligence and Machine Learning with Applications in Remote Sensing (Third Edition)

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1122

Special Issue Editors

Dr. Ying-Nong Chen

E-Mail Website
Guest Editor
Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan
Interests: hyperspectral; multispectral signal processing; machine learning; deep learning; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kuo-Chin Fan

E-Mail Website
Guest Editor
Department of Computer Science & Information Engineering, National Central University, Taoyuan 32001, Taiwan
Interests: remote sensing; artificial intelligence; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yang-Lang Chang

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: remote sensing; high performance computing; deep learning; pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Toshifumi Moriyama

E-Mail Website
Guest Editor
Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan
Interests: radar polarimetry; inverse scattering; microwave remote-sensing; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, with the advancement of technology, there are more and more data with higher spectral, spatial and temporal resolutions obtained from active and passive sensors. In addition, the applications of remote sensing data in environmental, commercial and military fields are becoming more and more popular. This poses challenges to effectively and efficiently process big remote sensing data. In recent years, many useful feature mining algorithms, deep learning algorithms, and decision tree inspired algorithms for remote sensing data processing has drawn a lot of researchers and received unprecedented popularity. Even with so many works and algorithms have been devoted to this popular topic, there is still so much we can do about artificial intelligence, machine learning and deep learning. Therefore, this Special Issue of Remote Sensing aims to demonstrate state-of-the-art works in employing artificial intelligence machine learning and deep learning algorithms for effective and efficient remote sensing applications. Papers are solicited in, but not limited to, the following areas:

  • Hyperspectral, multispectral applications with machine learning, deep learning algorithms;
  • Remote sensing data processing based on artificial intelligence and machine learning;
  • Hyperspectral, multispectral image processing;
  • AI/Deep learning/Machine learning for big hyperspectral, multispectral data analysis;
  • Remote sensing data for disasters, weather, water and climate applications based on AI/DL/ML algorithms;
  • Deep learning-based transfer learning;
  • Feature extraction with machine learning or deep learning for remote sensing data.

This is the Third Edition of the Special Issue, and experts and scholars in related fields are welcome to submit their original works to this Special Issue.

https://www.mdpi.com/journal/remotesensing/special_issues/AI_ML_Applications

Dr. Ying-Nong Chen
Prof. Dr. Kuo-Chin Fan
Prof. Dr. Yang-Lang Chang
Prof. Dr. Toshifumi Moriyama
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

  • remote sensing data
  • artificial intelligence
  • machine learning
  • deep learning
  • hyperspectral images

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 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, 3027 KiB  
Article
Multi-Directional Dual-Window Method Using Fractional Optimal-Order Fourier Transform for Hyperspectral Anomaly Detection
by Jiahui Wang, Fang Li, Liguo Wang and Jianjun He
Remote Sens. 2025, 17(8), 1321; https://doi.org/10.3390/rs17081321 - 8 Apr 2025
Viewed by 244
Abstract
Anomaly detection plays a vital role in the processing of hyperspectral images and has garnered significant attention recently. Hyperspectral images are characterized by their “integration of spatial and spectral information” as well as their rich spectral content. Therefore, effectively combining the spatial and [...] Read more.
Anomaly detection plays a vital role in the processing of hyperspectral images and has garnered significant attention recently. Hyperspectral images are characterized by their “integration of spatial and spectral information” as well as their rich spectral content. Therefore, effectively combining the spatial and spectral information of images and thoroughly mining the latent structural features of the data to achieve high-precision detection are significant challenges in hyperspectral anomaly detection. Traditional detection methods, which rely solely on raw spectral features, often face limitations in enhancing target signals and suppressing background noise. To address these issues, we propose an innovative hyperspectral anomaly detection approach based on the fractional optimal-order Fourier transform combined with a multi-directional dual-window detector. First, a new criterion for determining the optimal order of the fractional Fourier transform is introduced. By applying the optimal fractional Fourier transform, prominent features are extracted from the hyperspectral data. Subsequently, band selection is applied to the transformed data to remove redundant information and retain critical features. Additionally, a multi-directional sliding dual-window RAD detector is designed. This detector fully utilizes the spectral information of the pixel under test along with its neighboring information in eight directions to enhance detection accuracy. Furthermore, a spatial–spectral combined saliency-weighted strategy is developed to fuse the detection results from various directions using weighted contributions, further improving the distinction between anomalies and the background. The proposed method’s experimental results on six classic datasets demonstrate that it outperforms existing detectors, achieving superior detection performance. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing (Third Edition))
►▼ Show Figures

Figure 1

19 pages, 9088 KiB  
Article
An Approach for Multi-Source Land Use and Land Cover Data Fusion Considering Spatial Correlations
by Jing Yang, Yiheng Jiang, Qirui Song, Zheng Wang, Yang Hu, Kaiqiang Li and Yizhong Sun
Remote Sens. 2025, 17(7), 1131; https://doi.org/10.3390/rs17071131 - 22 Mar 2025
Viewed by 440
Abstract
As one of the foundational datasets in geographical information science, land use and land cover (LULC) data plays a crucial role in the study of human–environment interaction mechanisms, urban sustainable development, and other related issues. Although existing research has explored land use type [...] Read more.
As one of the foundational datasets in geographical information science, land use and land cover (LULC) data plays a crucial role in the study of human–environment interaction mechanisms, urban sustainable development, and other related issues. Although existing research has explored land use type recognition from remote sensing imagery, interpretation algorithms, and other perspectives, significant spatial discrepancies exist between these data products. Therefore, we introduced a multi-source LULC data integration approach that incorporates spatial dependencies, employing a fully connected neural network alongside geographical environmental variables to enhance the accuracy of land use data. The Yangtze River Delta was chosen as the case study area for method evaluation and validation. Our results show that the proposed method significantly improves land use classification accuracy. A comparative analysis from both global and category-specific perspectives revealed that the data product obtained exhibited notably higher overall accuracy, Kappa coefficient, and intersection over union compared to the China land cover dataset, the global 30 m fine land cover dynamic monitoring dataset, and the multi-period land use remote sensing monitoring dataset. Additionally, both the quantity and allocation disagreements of the fused LULC data were improved. The proposed multi-source land use data fusion method and its products can provide support and services for urban sustainable construction, resource management, and environmental monitoring and protection, demonstrating significant research value and importance. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing (Third Edition))
►▼ Show Figures

Graphical abstract

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