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Remote Sensing
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Advances in Multi-Source Remote Sensing Data Fusion and Analysis
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Advances in Multi-Source Remote Sensing Data Fusion and Analysis

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 656

Special Issue Editors

Dr. Jiahui Qu

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Service Network, Xidian University, Xi’an 710071, China
Interests: multi-source remote sensing image; deep learning; image fusion; change detection
Prof. Dr. Qian Du

grade E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yunsong Li

E-Mail Website
Guest Editor
The state key laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
Interests: image/video processing; coding and transmission; chip design; high-performance computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kun Tan

E-Mail Website
Guest Editor
The School of Geospatial Artificial Intelligence, East China Normal University, Shanghai 200241, China
Interests: hyperspectral remote sensing; environmental remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiangtao Peng

E-Mail Website
Guest Editor
Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan, China
Interests: machine learning; hyperspectral image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of aerospace and sensor technologies, the costs of satellite manufacturing and launch have significantly decreased, making it possible to obtain types of various remote sensing data, such as hyperspectral, panchromatic, LiDAR, and SAR data. The remote sensing data from different sensors provide a more comprehensive representation of ground objects on the Earth’s surface, showing potential in resource management, disaster detection, and other applications. However, the increasing variety of multi-source remote sensing data also imposes higher demands on data processing techniques. Due to the differences in imaging principles, sensor parameters, and atmospheric conditions among various remote sensing platforms, it is crucial to develop advanced methods for multi-source remote sensing image fusion and analysis, thus generating reliable quantitative results for practical applications.

This Special Issue aims to share valuable and rigorous research related to multi-source remote sensing data processing topics such as fusion, classification, change detection, etc. Advanced methods will be presented, including deep learning-based techniques for remote sensing data acquired from different sensors, providing novel solutions to leverage the complementary advantages of multi-source remote sensing data. Studies on datasets involving multi-source remote sensing data will also be covered.

Topics of interest include, but are not limited to, the following:

  • Remote sensing data pre-processing (e.g. pansharpening, super-resolution, fusion);
  • Multi-source remote sensing data for land cover mapping/classification;
  • Multi-temporal remote sensing data change detection;
  • Remote sensing data anomaly detection and target detection;
  • Deep fusion for multi-source remote sensing data analysis and understanding;
  • Advanced deep learning methods for multi-source remote sensing data processing;
  • Datasets for multi-source data fusion, classification, change detection or anomaly detection in remote sensing;
  • Challenges in multi-source remote sensing data processing.

Dr. Jiahui Qu
Prof. Dr. Qian Du
Prof. Dr. Yunsong Li
Prof. Dr. Kun Tan
Prof. Dr. Jiangtao Peng
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

  • multi-source remote sensing data
  • deep learning
  • multi-source image fusion
  • pansharpening
  • classification
  • change detection
  • anomaly detection
  • target detection

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Published Papers (1 paper)

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Research

25 pages, 1339 KiB  
Article
Link-State-Aware Proactive Data Delivery in Integrated Satellite–Terrestrial Networks for Multi-Modal Remote Sensing
by Ranshu Peng, Chunjiang Bian, Shi Chen and Min Wu
Remote Sens. 2025, 17(11), 1905; https://doi.org/10.3390/rs17111905 - 30 May 2025
Viewed by 269
Abstract
This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning [...] Read more.
This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite–terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches—a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns—jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite–terrestrial environments. Full article
(This article belongs to the Special Issue Advances in Multi-Source Remote Sensing Data Fusion and Analysis)
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