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Remote Sensing Image Fusion and Object Tracking
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Remote Sensing Image Fusion and Object Tracking

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 2416

Special Issue Editors

Dr. Xu Li

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: image fusion; remote sensing image classification; object tracking
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tao Gao

E-Mail Website
Guest Editor
School of Data Science and Artificial Intelligence, Chang’an University, Xi’an 710064, China
Interests: image processing; computer vision; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of sensor technology, remote sensing images are becoming increasingly diverse, constantly posing challenges for image fusion and object tracking research. This Special Issue aims to study the progress of remote sensing image fusion and object tracking technology, promote the advancement of remote sensing information processing, and benefit more readers and related researchers. Authors are sincerely invited to contribute to the research on state-of-the-art technologies. Articles may cover, but are not limited to, the following topics:

  1. Reviews of image fusion;
  2. Multi-source image fusion;
  3. Multimodal image fusion;
  4. Multitemporal image fusion;
  5. Multiangle image fusion;
  6. Multispectral/hyperspectral sharpening;
  7. The quality assessment of fusion;
  8. The application of remote sensing image fusion;
  9. The tracking of satellite videos;
  10. Hyperspectral tracking;
  11. Infrared and optical image fusion for object recognition and tracking;
  12. SAR/PolSAR image and hyperspectral image fusion for classification;
  13. RGB-Thermal tracking;
  14. RGB-Depth tracking;
  15. Small object tracking;
  16. Visual tracking.

Dr. Xu Li
Prof. Dr. Tao Gao
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. Sensors 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 2600 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
  • image fusion
  • object tracking
  • machine learning
  • deep learning
  • object detection
  • feature extraction
  • computer vision
  • pattern recognition

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Published Papers (3 papers)

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Research

17 pages, 872 KB  
Article
BATFNet: Boundary-Aware Transformer Fusion Network for RGB-DSM Semantic Segmentation of Remote Sensing Images
by Yilin Tong, Meng Tang, Yu Zhang, Yan Huang, Jing Huang, Yuelin He, Yuxin Liu, Edore Akpokodje and Dan Zheng
Sensors 2026, 26(10), 3205; https://doi.org/10.3390/s26103205 - 19 May 2026
Abstract
Semantic segmentation of very-high-resolution remote sensing imagery benefits from combining RGB appearance with Digital Surface Model (DSM) height information, especially in urban scenes where spectrally similar objects often differ in elevation. On the ISPRS Vaihingen and Potsdam benchmarks, BATFNet achieves mIoU scores of [...] Read more.
Semantic segmentation of very-high-resolution remote sensing imagery benefits from combining RGB appearance with Digital Surface Model (DSM) height information, especially in urban scenes where spectrally similar objects often differ in elevation. On the ISPRS Vaihingen and Potsdam benchmarks, BATFNet achieves mIoU scores of 84.06% and 85.31%, respectively, outperforming representative RGB–DSM fusion baselines on most land-cover categories. BATFNet is a supervised boundary-aware Transformer fusion network that uses DSM-derived edge priors to guide bidirectional cross-modal attention and decoder refinement. With a dual-branch ResNet-50 backbone for modality-specific feature extraction, the proposed framework effectively integrates RGB and DSM information while recovering fine spatial details. These results show that exploiting DSM-derived structural cues improves boundary delineation and reduces confusion among spectrally similar urban classes. Full article
(This article belongs to the Special Issue Remote Sensing Image Fusion and Object Tracking)
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26 pages, 3435 KB  
Article
Young White Pine Detection Using UAV Imagery and Deep Learning Object Detection Models
by Abishek Poudel and Eddie Bevilacqua
Sensors 2026, 26(4), 1284; https://doi.org/10.3390/s26041284 - 16 Feb 2026
Viewed by 521
Abstract
This study demonstrates the power of combining unmanned aerial vehicle (UAV) imagery and deep learning (DL) for monitoring forest regeneration, specifically focusing on young white pine (Pinus strobus). Using high-resolution three-band RGB and five-band multispectral orthomosaics derived from UAV flights, 20 [...] Read more.
This study demonstrates the power of combining unmanned aerial vehicle (UAV) imagery and deep learning (DL) for monitoring forest regeneration, specifically focusing on young white pine (Pinus strobus). Using high-resolution three-band RGB and five-band multispectral orthomosaics derived from UAV flights, 20 DL object-detection models were evaluated within ArcGIS Pro 3.4 software (Esri Inc., Redlands, CA, USA). The models were tested across study sites in St. Lawrence County, NY, to assess performance on three distinct size classes of white pine, each stratified into low, medium, and high density areas. The Faster R-CNN (F-RCNN) model, particularly when trained with image rotation and no augmentation, significantly outperformed others, achieving an average precision of 0.88 across both imagery types. Subsequent confusion matrix analysis yielded 91% and 90% overall accuracy in medium and high-density white pine blocks, respectively. These findings validate the use of UAV-DL systems as an accurate and efficient tool for operational white pine regeneration assessment, reducing the need for labor-intensive fieldwork. Full article
(This article belongs to the Special Issue Remote Sensing Image Fusion and Object Tracking)
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33 pages, 21513 KB  
Article
A No-Reference Multivariate Gaussian-Based Spectral Distortion Index for Pansharpened Images
by Bishr Omer Abdelrahman Adam, Xu Li, Jingying Wu and Xiankun Hao
Sensors 2026, 26(3), 1002; https://doi.org/10.3390/s26031002 - 3 Feb 2026
Viewed by 587
Abstract
Pansharpening is a fundamental image fusion technique used to enhance the spatial resolution of remote sensing imagery; however, it inevitably introduces spectral distortions that compromise the reliability of downstream analyses. Existing no-reference (NR) quality assessment methods often fail to exclusively isolate these spectral [...] Read more.
Pansharpening is a fundamental image fusion technique used to enhance the spatial resolution of remote sensing imagery; however, it inevitably introduces spectral distortions that compromise the reliability of downstream analyses. Existing no-reference (NR) quality assessment methods often fail to exclusively isolate these spectral errors from spatial artifacts or lack sensitivity to specific radiometric inconsistencies. To address this gap, this paper proposes a novel No-Reference Multivariate Gaussian-based Spectral Distortion Index (MVG-SDI) specifically designed for pansharpened images. The methodology extracts a hybrid feature set, combining First Digit Distribution (FDD) features derived from Benford’s Law in the hyperspherical color space (HCS) and Color Moment (CM) features. These features are then used to fit Multivariate Gaussian (MVG) models to both the original multispectral and fused images, with spectral distortion quantified via the Mahalanobis distance between their statistical parameters. Experiments on the NBU dataset showed that the MVG-SDI correlates more strongly with standard full-reference benchmarks (such as SAM and CC) than existing NR methods like QNR. Tests with simulated distortions confirmed that the proposed index remains stable and accurate even when facing specific spectral degradations like hue shifts or saturation changes. Full article
(This article belongs to the Special Issue Remote Sensing Image Fusion and Object Tracking)
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