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Remote Sensing
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Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 15 April 2025 | Viewed by 8176

Special Issue Editors

Prof. Dr. Yuhui Zheng

E-Mail Website
Guest Editor
Nanjing University of Information Science & Technology, Nanjing, China
Interests: Computer Vision; Multimedia Forensics; Digital
Special Issues, Collections and Topics in MDPI journals
Dr. Guoqing Zhang

E-Mail Website
Guest Editor
School of Computer Science, Nanjing University of Information Science and Technology, No. 219 Ningliu Road, Nanjing 210044, Jiangsu Province, China
Interests: Computer vision; multispectral image processing; person Re-identification; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Le Sun

E-Mail Website
Guest Editor
School of Computer and Software, Nanjing University of Information Science and Technology, No. 219 Ningliu Road, Nanjing 210044, Jiangsu Province, China
Interests: hyperspectral remote sensing image processing (including: unmixing, classification, fusion); deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Byeungwoo Jeon

E-Mail Website
Guest Editor
Dean of Information & Communication Engineering College
Interests: Hyperspectral Imagery; Image Denoising; Spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second edition of the Special Issue titled “Multi-Platform and Multi-Modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques”. After the resounding success of our first edition, we are thrilled to launch the second edition.

Recent advances in sensor and aircraft technology have enabled us to acquire vast amounts of different types of remote sensing data for Earth observation. These multi-source data make it possible to derive diverse information regarding the Earth’s surface. For instance, multispectral and hyperspectral images can provide rich spectral information about geound objects, panchromatic images can reach fine spatial resolutions, synthetic aperture radar (SAR) data can be used to map different properties of the terrain, and laser imaging detection and ranging (LIDAR) data can clarify the elevation of land cover. However, a single source of data can no longer meet the needs of subsequent processing, such as classification, object detection/tracking, super-resolution, and restoration.

Therefore, multi-modal remote sensing data, acquired using sensors from multiple platforms, should be combined and fused. This fusion can make full use of the complementary information of multi-source remote sensing data, thereby further improving the accuracy of the analysis of the acquired scene (classification, detection, tracking, geological mapping, etc.).

Recently, deep learning has become one of the hottest research fields. Many advanced deep learning techniques have been developed, such as meta learning, self-supervision learning, few-shot learning, evolutionary learning, attention mechanisms, transformer, etc. The application of these technologies to remote sensing images, especially the fusion of multi-platform and multi-modal remote sensing data, is still an open topic. For this Special Issue, we desire original contributions (including high-quality original research articles, reviews, theoretical and critical perspectives, and viewpoint articles) written by innovative researchers on the fusion of multi-platform and multi-modal remote sensing data, which exploits advanced deep learning techniques to address the aforementioned theoretical and practical problems.

Prof. Dr. Yuhui Zheng
Dr. Guoqing Zhang
Dr. Le Sun
Prof. Dr. Byeungwoo Jeon
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

  • multispectral and hyperspectral data fusion
  • hyperspectral and LiDAR data fusion
  • pansharpening or thermal sharpening
  • optical and SAR data fusion
  • optical and LiDAR data fusion
  • novel benchmark
  • multi-platform or multi-modal datasets
  • advanced deep learning algorithm/architectures/theory transfer, multitask, few-shot and meta learning
  • attention mechanism and transformer
  • convolutional neural networks/graph convolutional networks
  • scene/object classification and segmentation
  • target detection/tracking
  • geological mapping

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Related Special Issue

Published Papers (5 papers)

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Research

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20 pages, 10176 KiB  
Article
DHQ-DETR: Distributed and High-Quality Object Query for Enhanced Dense Detection in Remote Sensing
by Chenglong Li, Jianwei Zhang, Bihan Huo and Yingjian Xue
Remote Sens. 2025, 17(3), 514; https://doi.org/10.3390/rs17030514 - 1 Feb 2025
Viewed by 243
Abstract
With the widespread application of remote sensing technologies and UAV imagery in various fields, dense object detection has become a significant and challenging task in computer vision research. Existing end-to-end detection models, particularly those based on DETR, often face criticism due to their [...] Read more.
With the widespread application of remote sensing technologies and UAV imagery in various fields, dense object detection has become a significant and challenging task in computer vision research. Existing end-to-end detection models, particularly those based on DETR, often face criticism due to their high computational demands, slow convergence rates, and inadequacy in managing dense, multi-scale objects. These challenges are especially acute in remote sensing applications, where accurate analysis of large-scale aerial and satellite imagery relies heavily on effective dense object detection. In this paper, we propose the DHQ-DETR framework, which addresses these issues by modeling bounding box offsets as distributions. DHQ-DETR incorporates the Distribution Focus Loss (DFL) to enhance residual learning, and introduces a High-Quality Query Selection (HQQS) module to effectively balance classification and regression tasks. Additionally, we propose an auxiliary detection head and a sample assignment strategy that complements the Hungarian algorithm to accelerate convergence. Our experimental results demonstrate the superior performance of DHQ-DETR, achieving an average precision (AP) of 53.7% on the COCO val2017 dataset, 54.3% on the DOTAv1.0, and 32.4% on Visdrone, underscoring its effectiveness for real-world dense object detection tasks. Full article
(This article belongs to the Special Issue Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition))
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21 pages, 1295 KiB  
Article
Co-LLaVA: Efficient Remote Sensing Visual Question Answering via Model Collaboration
by Fan Liu, Wenwen Dai, Chuanyi Zhang, Jiale Zhu, Liang Yao and Xin Li
Remote Sens. 2025, 17(3), 466; https://doi.org/10.3390/rs17030466 - 29 Jan 2025
Viewed by 271
Abstract
Large vision language models (LVLMs) are built upon large language models (LLMs) and incorporate non-textual modalities; they can perform various multimodal tasks. Applying LVLMs in remote sensing (RS) visual question answering (VQA) tasks can take advantage of the powerful capabilities to promote the [...] Read more.
Large vision language models (LVLMs) are built upon large language models (LLMs) and incorporate non-textual modalities; they can perform various multimodal tasks. Applying LVLMs in remote sensing (RS) visual question answering (VQA) tasks can take advantage of the powerful capabilities to promote the development of VQA in RS. However, due to the greater complexity of remote sensing images compared to natural images, general-domain LVLMs tend to perform poorly in RS scenarios and are prone to hallucination phenomena. Multi-agent debate for collaborative reasoning is commonly utilized to mitigate hallucination phenomena. Although this method is effective, it comes with a significant computational burden (e.g., high CPU/GPU demands and slow inference speed). To address these limitations, we propose Co-LLaVA, a model specifically designed for RS VQA tasks. Specifically, Co-LLaVA employs model collaboration between Large Language and Vision Assistant (LLaVA-v1.5) and Contrastive Captioners (CoCas). It combines LVLM with a lightweight generative model, reducing computational burden compared to multi-agent debate. Additionally, through high-dimensional multi-scale features and higher-resolution images, Co-LLaVA can enhance the perception of details in RS images. Experimental results demonstrate the significant performance improvements of our Co-LLaVA over existing LVLMs (e.g., Geochat, RSGPT) on multiple metrics of four RS VQA datasets (e.g., +3% over SkySenseGPT on “Rural/Urban” accuracy in the test set of RSVQA-LR dataset). Full article
(This article belongs to the Special Issue Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition))
22 pages, 23478 KiB  
Article
Target Detection and Characterization of Multi-Platform Remote Sensing Data
by Koushikey Chhapariya, Emmett Ientilucci, Krishna Mohan Buddhiraju and Anil Kumar
Remote Sens. 2024, 16(24), 4729; https://doi.org/10.3390/rs16244729 - 18 Dec 2024
Viewed by 800
Abstract
Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, [...] Read more.
Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration. Full article
(This article belongs to the Special Issue Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition))
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17 pages, 2648 KiB  
Article
Multi-Feature Cross Attention-Induced Transformer Network for Hyperspectral and LiDAR Data Classification
by Zirui Li, Runbang Liu, Le Sun and Yuhui Zheng
Remote Sens. 2024, 16(15), 2775; https://doi.org/10.3390/rs16152775 - 29 Jul 2024
Cited by 1 | Viewed by 2253
Abstract
Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed [...] Read more.
Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed on how to more deeply integrate the features of two modalities in attention mechanisms. In this paper, we propose a novel Multi-Feature Cross Attention-Induced Transformer Network (MCAITN) designed to enhance the classification accuracy of hyperspectral and LiDAR data. The MCAITN integrates the strengths of both data modalities by leveraging a cross-attention mechanism that effectively captures the complementary information between hyperspectral and LiDAR features. By utilizing a transformer-based architecture, the network is capable of learning complex spatial-spectral relationships and long-range dependencies. The cross-attention module facilitates the fusion of multi-source data, improving the network’s ability to discriminate between different land cover types. Extensive experiments conducted on benchmark datasets demonstrate that the MCAITN outperforms state-of-the-art methods in terms of classification accuracy and robustness. Full article
(This article belongs to the Special Issue Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition))
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Review

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32 pages, 25887 KiB  
Review
Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review
by Souad Saidi, Soufiane Idbraim, Younes Karmoude, Antoine Masse and Manuel Arbelo
Remote Sens. 2024, 16(20), 3852; https://doi.org/10.3390/rs16203852 - 17 Oct 2024
Cited by 3 | Viewed by 3877
Abstract
Remote sensing images provide a valuable way to observe the Earth’s surface and identify objects from a satellite or airborne perspective. Researchers can gain a more comprehensive understanding of the Earth’s surface by using a variety of heterogeneous data sources, including multispectral, hyperspectral, [...] Read more.
Remote sensing images provide a valuable way to observe the Earth’s surface and identify objects from a satellite or airborne perspective. Researchers can gain a more comprehensive understanding of the Earth’s surface by using a variety of heterogeneous data sources, including multispectral, hyperspectral, radar, and multitemporal imagery. This abundance of different information over a specified area offers an opportunity to significantly improve change detection tasks by merging or fusing these sources. This review explores the application of deep learning for change detection in remote sensing imagery, encompassing both homogeneous and heterogeneous scenes. It delves into publicly available datasets specifically designed for this task, analyzes selected deep learning models employed for change detection, and explores current challenges and trends in the field, concluding with a look towards potential future developments. Full article
(This article belongs to the Special Issue Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition))
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