Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Machine Learning and Computational Intelligence in Remote Sensing
share announcement

Machine Learning and Computational Intelligence in Remote Sensing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2510

Special Issue Editors

Dr. Zhiheng Liu

E-Mail Website
Guest Editor
School of Aerospace Science and Technology, Xidian University, Xi’an 710126, China
Interests: remote sensing; image segmentation; image matching; object detection
Dr. Jianhua Guo

E-Mail Website
Guest Editor
Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany
Interests: remote sensing; computer vision; deep learning; urban ecosystem services
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaobo Li

E-Mail Website
Guest Editor
School of Marine Science and Technology, Tianjin University, Tianjin 300054, China
Interests: polarization optics (polarimetry and polarimetric imaging); oceanic optics; deep learning and signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Suiping Zhou

E-Mail Website
Guest Editor
School of Aerospace Science and Technology, Xidian University, Xi’an 710126, China
Interests: large-scale distributed interactive applications; parallel/distributed systems; and big data in space science and technology
Dr. Tingting Wu

E-Mail Website
Guest Editor
School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Interests: remote sensing; image segmentation; cloud detection; machine learning

Special Issue Information

Dear Colleagues,

Remote sensing is in an exciting technological phase, where the integration of machine learning and computational intelligence provides unprecedented opportunities for processing and analyzing large-scale remote sensing data. From satellite imagery to drone photography, the diversity and complexity of remote sensing data require us to adopt advanced algorithms and models to extract useful information and solve practical problems. With the rapid development of remote sensing technology, our needs and capabilities for Earth observation are continuously increasing. Machine learning and computational intelligence, as powerful tools, are changing the way remote sensing data are analyzed and enhancing our understanding of complex Earth systems. This Special Issue aims to gather and showcase the latest research findings in this field, promote academic exchange, and advance the science of remote sensing.

The goal of this Special Issue is to provide an interdisciplinary communication platform, to develop new machine learning algorithms adapted to the characteristics of remote sensing data. Explore automated and intelligent data preprocessing, feature extraction, and classification methods. Train and validate machine learning models using remote sensing datasets. Share application examples of machine learning in agriculture, forestry, urban development, environmental monitoring, and other fields. Articles may address, but are not limited to, the following topics:

  • Application of machine learning in the fusion of multi-source remote sensing data.
  • Optimization of deep learning network structures for remote sensing image classification and object recognition.
  • New methods and strategies of computational intelligence in remote sensing data processing.
  • Automated feature extraction and pattern recognition in remote sensing data.
  • Spatiotemporal analysis and dynamic monitoring of remote sensing data.
  • Applications of remote sensing data in climate change, disaster response, and sustainable development.

Dr. Zhiheng Liu
Dr. Jianhua Guo
Dr. Xiaobo Li
Prof. Dr. Suiping Zhou
Dr. Tingting Wu
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. Electronics 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 2400 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

  • machine learning
  • computational intelligence
  • remote sensing
  • data analysis
  • earth observation

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.

Published Papers (3 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, 74740 KiB  
Article
Dual-Task Learning for Long-Range Classification in Single-Pixel Imaging Under Atmospheric Turbulence
by Yusen Liao, Yin Cheng and Jun Ke
Electronics 2025, 14(7), 1355; https://doi.org/10.3390/electronics14071355 - 28 Mar 2025
Viewed by 243
Abstract
Unlike traditional imaging, single-pixel imaging (SPI) exhibits greater resistance to atmospheric turbulence. Therefore, we use SPI for long-range classification, in which atmospheric turbulence often cause significant degradation in performance. We propose a dual-task learning method for SPI classification. Specifically, we design the Long-Range [...] Read more.
Unlike traditional imaging, single-pixel imaging (SPI) exhibits greater resistance to atmospheric turbulence. Therefore, we use SPI for long-range classification, in which atmospheric turbulence often cause significant degradation in performance. We propose a dual-task learning method for SPI classification. Specifically, we design the Long-Range Dual-Task Single-Pixel Network (LR-DTSPNet) to perform object classification and image restoration simultaneously, enhancing the model’s generalization and robustness. Attention mechanisms and residual convolutions are used to strengthen feature modeling and improve classification performance on low-resolution images. To improve the efficiency of SPI, low-resolution objects are used in this work. Experimental results on the DOTA remote sensing dataset demonstrate that our method significantly outperforms conventional object classification approaches. Furthermore, our approach holds promise for delivering high-quality images that are applicable to other computer vision tasks. Full article
(This article belongs to the Special Issue Machine Learning and Computational Intelligence in Remote Sensing)
►▼ Show Figures

Figure 1

27 pages, 15796 KiB  
Article
MSFF: A Multi-Scale Feature Fusion Convolutional Neural Network for Hyperspectral Image Classification
by Gu Gong, Xiaopeng Wang, Jiahua Zhang, Xiaodi Shang, Zhicheng Pan, Zhiyuan Li and Junshi Zhang
Electronics 2025, 14(4), 797; https://doi.org/10.3390/electronics14040797 - 18 Feb 2025
Cited by 1 | Viewed by 734
Abstract
In contrast to conventional remote sensing images, hyperspectral remote sensing images are characterized by a greater number of spectral bands and exceptionally high resolution. The richness of both spectral and spatial information facilitates the precise classification of various objects within the images, establishing [...] Read more.
In contrast to conventional remote sensing images, hyperspectral remote sensing images are characterized by a greater number of spectral bands and exceptionally high resolution. The richness of both spectral and spatial information facilitates the precise classification of various objects within the images, establishing hyperspectral imaging as indispensable for remote sensing applications. However, the labor-intensive and time-consuming process of labeling hyperspectral images results in limited labeled samples, while challenges like spectral similarity between different objects and spectral variation within the same object further complicate the development of classification algorithms. Therefore, efficiently exploiting the spatial and spectral information in hyperspectral images is crucial for accomplishing the classification task. To address these challenges, this paper presents a multi-scale feature fusion convolutional neural network (MSFF). The network introduces a dual branch spectral and spatial feature extraction module utilizing 3D depthwise separable convolution for joint spectral and spatial feature extraction, further refined by an attention-based-on-central-pixels (ACP) mechanism. Additionally, a spectral–spatial joint attention module (SSJA) is designed to interactively explore latent dependency between spectral and spatial information through the use of multilayer perceptron and global pooling operations. Finally, a feature fusion module (FF) and an adaptive multi-scale feature extraction module (AMSFE) are incorporated to enable adaptive feature fusion and comprehensive mining of feature information. Experimental results demonstrate that the proposed method performs exceptionally well on the IP, PU, and YRE datasets, delivering superior classification results compared to other methods and underscoring the potential and advantages of MSFF in hyperspectral remote sensing classification. Full article
(This article belongs to the Special Issue Machine Learning and Computational Intelligence in Remote Sensing)
►▼ Show Figures

Figure 1

22 pages, 13121 KiB  
Article
Research on Irrigation Grade Discrimination Method Based on Semantic Segmentation
by Xibao Wu, Wentao Chen, Kexin Yang, Xin Zhao, Yiqun Wang and Wenbai Chen
Electronics 2024, 13(23), 4629; https://doi.org/10.3390/electronics13234629 - 23 Nov 2024
Viewed by 947
Abstract
As one of China’s major grain crops, wheat has a high demand for water resources, making it susceptible to drought stress. Traditional irrigation evaluation methods are often based on experience and rule-based calculations, which struggle to cope with complex environmental factors and dynamic [...] Read more.
As one of China’s major grain crops, wheat has a high demand for water resources, making it susceptible to drought stress. Traditional irrigation evaluation methods are often based on experience and rule-based calculations, which struggle to cope with complex environmental factors and dynamic changes in crop needs. With technological advancements, deep learning-based research methods, characterized by their strong data-driven analytical capabilities, are expected to improve the accuracy of evaluation results. This paper focuses on the irrigation demand assessment of winter wheat farmland, aiming to explore a new regional-scale irrigation demand assessment method based on deep learning. By establishing samples of different irrigation evaluation levels, this study seeks to better meet the requirements of irrigation demand assessment. For the problem of regional-scale irrigation-level discrimination, the Convolutional Network Attention(CONAT) module was proposed to optimize the backbone network structure of the Mask2Former model. To tackle issues related to data imbalance and underfitting across certain categories, a loss function tailored for imbalanced sample distributions was implemented, accompanied by enhancements to the training scheme. By contrasting this refined model with alternative methods for discriminating irrigation levels, the viability of this approach was showcased. Full article
(This article belongs to the Special Issue Machine Learning and Computational Intelligence in Remote Sensing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop