Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.6
Journals
Electronics
Special Issues
Challenges and Prospects in Remote Sensing Data Intelligent Interpretation
share announcement

Challenges and Prospects in Remote Sensing Data Intelligent Interpretation

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

Deadline for manuscript submissions: 15 November 2026 | Viewed by 643

Special Issue Editors

Dr. Xiaoli Li

E-Mail Website
Guest Editor
Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen 518055, China
Interests: remote sensing; ecological remote sensing; image segmentation and classification; forest remote sensing
Dr. Kun Hu

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: intelligent image processing and applications; SAR image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xupei Zhang

E-Mail Website
Guest Editor
School of Optoelectronic Engineering, Xidian University, Xi’an 710071, China
Interests: multi-modal image fusion; image processing; vision navigation based on remote sensing image; remote sensing image generation and translation
Prof. Dr. Jinsong Chen

E-Mail Website
Guest Editor
Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen 518055, China
Interests: remote sensing; ecological environment monitoring; spatial analysis; data assimilation; data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing, as an important method of earth observation, plays an indispensable role in various fields, such as resource investigation, environmental monitoring, urban management, and disaster prevention and mitigation. With the rapid development of satellite and unmanned aerial vehicle (UAV) technologies, the types of remote sensing data have become increasingly diverse, including multispectral, hyperspectral, thermal infrared, SAR, and LiDAR data. They provide a rich data foundation for the interpretation of remote sensing. The development of machine learning and deep learning has provided advanced technological tools for land cover segmentation/classification, change detection, object detection and recognition, etc. However, in the context of remote sensing big data, with the increasing demand for accuracy, depth, and breadth of knowledge interpretation, the intelligent interpretation capability of remote sensing data is insufficient. This Special Issue aims to explore new theories, methods, and applications in remote sensing data intelligent interpretation, promote the development of remote sensing interpretation toward higher accuracy, finer granularity, and greater comprehensiveness, and provide stronger scientific and technological support for global sustainable development.

Potential topics of interest may include, but are not limited to, the following:

  1. Intelligent land cover segmentation or classification algorithm;
  2. Intelligent ground object change detection algorithm;
  3. Intelligent object detection and recognition algorithm;
  4. Intelligent motion tracking and analysis algorithm;
  5. Advanced multimodal data fusion algorithm;
  6. Advanced remote sensing large model optimization algorithm;
  7. Application in the field of resource investigation;
  8. Application in the field of environmental monitoring;
  9. Application in the field of urban management;
  10. Application in the field of disaster emergency response.

Dr. Xiaoli Li
Dr. Kun Hu
Dr. Xupei Zhang
Prof. Dr. Jinsong Chen
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. 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

  • remote sensing data intelligent interpretation
  • land cover segmentation or classification
  • change detection
  • object detection and recognition
  • remote sensing large models

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.

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

18 pages, 2017 KB  
Article
Optical Remote Sensing Image Classification Based on Quantum Statistics
by Xiaoli Li, Longlong Zhao, Hongzhong Li, Pan Chen, Luyi Sun, Shanxin Guo, Xuemei Zhao and Jinsong Chen
Electronics 2026, 15(10), 2075; https://doi.org/10.3390/electronics15102075 - 13 May 2026
Abstract
To address the difficulty of finely classifying complex optical remote sensing images, this paper innovatively proposes a new image classification method based on quantum statistics (QS) inspired by quantum physics. Each pixel in the image is regarded as a fermion, which is one [...] Read more.
To address the difficulty of finely classifying complex optical remote sensing images, this paper innovatively proposes a new image classification method based on quantum statistics (QS) inspired by quantum physics. Each pixel in the image is regarded as a fermion, which is one of the fundamental particles in quantum systems. The energy of the energy level where fermions are located is described using the negative logarithm of the distribution that the spectrum of the pixel follows. The Fermi-Dirac distribution, a quantum statistics model used to describe the complex occupation pattern of energy levels by fermions, is employed to characterize the membership relationship between pixels and classes, instead of traditional distance measures and probability measures. Then, the cost function guiding the convergence of classification is defined based on free energy, which is used to describe whether a system is in a state of thermal equilibrium according to energy, temperature, and entropy. To minimize the free energy, the derivative method and the simulated annealing algorithm are adopted to estimate the optimal solution for model parameters. The proposed method can describe complex features more effectively, obtain fine classification results, and overcome the curse of dimensionality in high-dimensional image classification. Finally, the feasibility and effectiveness are verified through qualitative and quantitative analysis of multispectral and hyperspectral image classification experiments. Full article
(This article belongs to the Special Issue Challenges and Prospects in Remote Sensing Data Intelligent Interpretation)
►▼ Show Figures

Figure 1

26 pages, 36181 KB  
Article
A Hybrid U-Net and Attention-Based BiLSTM Framework for Wildfire Prediction Using Multi-Source Remote Sensing and Meteorological Sensor Data
by Zhiyu Chen, Weiwei Song, Xiaoqing Zuo, Siyuan Li, Huyue Chen and Bowen Zuo
Electronics 2026, 15(9), 1893; https://doi.org/10.3390/electronics15091893 - 30 Apr 2026
Viewed by 270
Abstract
Forest and grassland fires have become increasingly severe under climate change, posing significant threats to ecosystems and human safety. Accurate wildfire prediction using remote sensing data remains challenging due to complex spatiotemporal dynamics and heterogeneous data sources. To address this issue, this study [...] Read more.
Forest and grassland fires have become increasingly severe under climate change, posing significant threats to ecosystems and human safety. Accurate wildfire prediction using remote sensing data remains challenging due to complex spatiotemporal dynamics and heterogeneous data sources. To address this issue, this study proposes a hybrid deep learning framework integrating U-Net and an attention-enhanced bidirectional long short-term memory network (AUBLSTM) for spatiotemporal wildfire prediction using multi-source remote sensing and meteorological data. The U-Net is employed for spatial feature extraction, while AUBLSTM captures temporal dependencies and improves fire spread modeling with attention mechanisms. An encoder–decoder architecture is adopted to enhance multi-scale feature representation, and meteorological constraints are incorporated to improve physical consistency. Experimental results demonstrate that the proposed model outperforms baseline methods, including convolutional long short-term memory (ConvLSTM) and fully connected networks, achieving superior performance in terms of MSE, RRMSE, PSNR, SSIM, IoU, and F1-Score. The framework is efficient, scalable, and suitable for deployment in electronic monitoring and early warning systems, providing a practical solution for integrating multi-source data into wildfire surveillance applications. Full article
(This article belongs to the Special Issue Challenges and Prospects in Remote Sensing Data Intelligent Interpretation)
►▼ 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