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Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 6775

Special Issue Editors

Prof. Dr. Wei Li

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: multi-source remote sensing data processing
Special Issues, Collections and Topics in MDPI journals
Dr. Haiyong Gan

E-Mail Website
Guest Editor
National Institute of Metrology, Beijing 100029, China
Interests: spectral irradiance analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Heng-Chao Li

E-Mail Website
Guest Editor
School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
Interests: remote sensing image processing and pattern recognition
Dr. Wenshuai Hu

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: remote sensing image processing and neural networks

Special Issue Information

Dear Colleagues,

With the development of remote sensing techniques and various remote sensing sensors/platforms (ground-based, UAV-based, satellite-based), information acquisition and intelligent processing technologies are showing a rapid and diversified development trend. Integrating multi-sensor data to implement comprehensive detection and analysis can compensate for the unreliability and inaccuracy of single-sensor systems in Earth observation, while machine learning technology can effectively solve various difficulties in intelligent interpretation of multi-source remote sensing data. Hence, research on machine learning for intelligent processing and applications of multi-source remote sensing data is currently a popular trend in the field of Earth observation.

This Special Issue is open to research on the intelligent processing of multi-source remote sensing data to identify current research trends and key issues, with the aim of compiling the latest research on how multi-source remote sensing data, machine learning, and other methods can effectively assist in various remote sensing applications.

This Special Issue calls for articles that focus on the development and applications of theory, processing methods, strategies, and new technologies using multi-source remote sensing data, including high-resolution visible light, panchromatic, infrared, multispectral, hyperspectral, synthetic aperture radar (SAR), light detection and ranging (LiDAR), etc. Potential topics may include, but are not limited to, the following:

  • Multi-source remote sensing data imaging (e.g., super-resolution, denoising, pansharpening, data fusion, etc.);
  • Cross-modal analysis in remote sensing, including spectral irradiance analysis, multi-source feature optimization, intelligent collaborative interpretation, etc.
  • Multi-source remote sensing processing (e.g., registration, fusion, feature extraction, classification, segmentation, object detection, etc.);
  • Machine learning methods as well as their lightweight designs for intelligent processing of multi-source remote sensing data.

Prof. Dr. Wei Li
Dr. Haiyong Gan
Prof. Dr. Heng-Chao Li
Dr. Wenshuai Hu
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
  • intelligent processing
  • earth observation applications
  • registration
  • data fusion
  • feature extraction
  • classification
  • object detection
  • advanced machine learning methods
  • lightweight designs

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

20 pages, 7255 KiB  
Article
Background-Aware Cross-Attention Multiscale Fusion for Multispectral Object Detection
by Runze Guo, Xiaojun Guo, Xiaoyong Sun, Peida Zhou, Bei Sun and Shaojing Su
Remote Sens. 2024, 16(21), 4034; https://doi.org/10.3390/rs16214034 - 30 Oct 2024
Viewed by 498
Abstract
Limited by the imaging capabilities of sensors, research based on single modality is difficult to cope with faults and dynamic perturbations in detection. Effective multispectral object detection, which can achieve better detection accuracy by fusing visual information from different modalities, has attracted widespread [...] Read more.
Limited by the imaging capabilities of sensors, research based on single modality is difficult to cope with faults and dynamic perturbations in detection. Effective multispectral object detection, which can achieve better detection accuracy by fusing visual information from different modalities, has attracted widespread attention. However, most of the existing methods adopt simple fusion mechanisms, which fail to utilize the complementary information between modalities while lacking the guidance of a priori knowledge. To address the above issues, we propose a novel background-aware cross-attention multiscale fusion network (BA-CAMF Net) to achieve adaptive fusion in visible and infrared images. First, a background-aware module is designed to calculate the light and contrast to guide the fusion. Then, a cross-attention multiscale fusion module is put forward to enhance inter-modality complement features and intra-modality intrinsic features. Finally, multiscale feature maps from different modalities are fused according to background-aware weights. Experimental results on LLVIP, FLIR, and VEDAI indicate that the proposed BA-CAMF Net achieves higher detection accuracy than the current State-of-the-Art multispectral detectors. Full article
(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)
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26 pages, 28365 KiB  
Article
Three-Dimensional Geometric-Physical Modeling of an Environment with an In-House-Developed Multi-Sensor Robotic System
by Su Zhang, Minglang Yu, Haoyu Chen, Minchao Zhang, Kai Tan, Xufeng Chen, Haipeng Wang and Feng Xu
Remote Sens. 2024, 16(20), 3897; https://doi.org/10.3390/rs16203897 - 20 Oct 2024
Viewed by 711
Abstract
Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral [...] Read more.
Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral camera to acquire the electromagnetic characteristics and material categories of the target environment and simultaneously employs light detection and ranging (LiDAR) and an optical camera to achieve a three-dimensional spatial reconstruction of the environment. Specifically, the millimeter-wave radar sensor adopts a multiple input multiple output (MIMO) array and obtains 3D synthetic aperture radar images through 1D mechanical scanning perpendicular to the array, thereby capturing the electromagnetic properties of the environment. The multispectral camera, equipped with nine channels, provides rich spectral information for material identification and clustering. Additionally, LiDAR is used to obtain a 3D point cloud, combined with the RGB images captured by the optical camera, enabling the construction of a three-dimensional geometric model. By fusing the data from four sensors, a comprehensive geometric-physical model of the environment can be constructed. Experiments conducted in indoor environments demonstrated excellent spatial-geometric-physical reconstruction results. This system can play an important role in various applications, such as environment modeling and planning. Full article
(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)
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25 pages, 27745 KiB  
Article
Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain
by Liangliang Li, Yan Shi, Ming Lv, Zhenhong Jia, Minqin Liu, Xiaobin Zhao, Xueyu Zhang and Hongbing Ma
Remote Sens. 2024, 16(20), 3804; https://doi.org/10.3390/rs16203804 - 13 Oct 2024
Cited by 1 | Viewed by 979
Abstract
The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, [...] Read more.
The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image. Full article
(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)
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20 pages, 27367 KiB  
Article
MCG-RTDETR: Multi-Convolution and Context-Guided Network with Cascaded Group Attention for Object Detection in Unmanned Aerial Vehicle Imagery
by Chushi Yu and Yoan Shin
Remote Sens. 2024, 16(17), 3169; https://doi.org/10.3390/rs16173169 - 27 Aug 2024
Cited by 1 | Viewed by 1092
Abstract
In recent years, object detection in unmanned aerial vehicle (UAV) imagery has been a prominent and crucial task, with advancements in drone and remote sensing technologies. However, detecting targets in UAV images pose challenges such as complex background, severe occlusion, dense small targets, [...] Read more.
In recent years, object detection in unmanned aerial vehicle (UAV) imagery has been a prominent and crucial task, with advancements in drone and remote sensing technologies. However, detecting targets in UAV images pose challenges such as complex background, severe occlusion, dense small targets, and lighting conditions. Despite the notable progress of object detection algorithms based on deep learning, they still struggle with missed detections and false alarms. In this work, we introduce an MCG-RTDETR approach based on the real-time detection transformer (RT-DETR) with dual and deformable convolution modules, a cascaded group attention module, a context-guided feature fusion structure with context-guided downsampling, and a more flexible prediction head for precise object detection in UAV imagery. Experimental outcomes on the VisDrone2019 dataset illustrate that our approach achieves the highest AP of 29.7% and AP50 of 58.2%, surpassing several cutting-edge algorithms. Visual results further validate the model’s robustness and capability in complex environments. Full article
(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)
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24 pages, 14867 KiB  
Article
CVTNet: A Fusion of Convolutional Neural Networks and Vision Transformer for Wetland Mapping Using Sentinel-1 and Sentinel-2 Satellite Data
by Mohammad Marjani, Masoud Mahdianpari, Fariba Mohammadimanesh and Eric W. Gill
Remote Sens. 2024, 16(13), 2427; https://doi.org/10.3390/rs16132427 - 2 Jul 2024
Cited by 3 | Viewed by 1276
Abstract
Wetland mapping is a critical component of environmental monitoring, requiring advanced techniques to accurately represent the complex land cover patterns and subtle class differences innate in these ecosystems. This study aims to address these challenges by proposing CVTNet, a novel deep learning (DL) [...] Read more.
Wetland mapping is a critical component of environmental monitoring, requiring advanced techniques to accurately represent the complex land cover patterns and subtle class differences innate in these ecosystems. This study aims to address these challenges by proposing CVTNet, a novel deep learning (DL) model that integrates convolutional neural networks (CNNs) and vision transformer (ViT) architectures. CVTNet uses channel attention (CA) and spatial attention (SA) mechanisms to enhance feature extraction from Sentinel-1 (S1) and Sentinel-2 (S2) satellite data. The primary goal of this model is to achieve a balanced trade-off between Precision and Recall, which is essential for accurate wetland mapping. The class-specific analysis demonstrated CVTNet’s proficiency across diverse classes, including pasture, shrubland, urban, bog, fen, and water. Comparative analysis showed that CVTNet outperforms contemporary algorithms such as Random Forest (RF), ViT, multi-layer perceptron mixer (MLP-mixer), and hybrid spectral net (HybridSN) classifiers. Additionally, the attention mechanism (AM) analysis and sensitivity analysis highlighted the crucial role of CA, SA, and ViT in focusing the model’s attention on critical regions, thereby improving the mapping of wetland regions. Despite challenges at class boundaries, particularly between bog and fen, and misclassifications of swamp pixels, CVTNet presents a solution for wetland mapping. Full article
(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)
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31 pages, 2478 KiB  
Article
Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors
by Marcel Kettelgerdes, Nicolas Sarmiento, Hüseyin Erdogan, Bernhard Wunderle and Gordon Elger
Remote Sens. 2024, 16(13), 2407; https://doi.org/10.3390/rs16132407 - 30 Jun 2024
Viewed by 1493
Abstract
With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due [...] Read more.
With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion. Full article
(This article belongs to the Special Issue Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data)
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