Algorithms in Multi-Sensor Imaging and Fusion

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2017

Special Issue Editors

Department of Computer Information Systems, State University of New York at Buffalo State, Buffalo, NY 14222, USA
Interests: computer vision; image processing; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: image color analysis; image enhancement; image fusion; image restoration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multi-sensor image fusion is a field dedicated to processing images of the same subject or scene captured by multiple sensors. This approach involves combining various sensors that provide diverse perspectives and spatial information to achieve a coherent interpretation of the observed environment. In recent years, multi-sensor image fusion has emerged as a vibrant area of research, witnessing the proposal of numerous fusion methods. These methods leverage effective techniques, like multi-scale transformation, fuzzy inference, and deep learning, to craft fusion algorithms.

Despite significant advancements, several notable challenges persist within this domain. These include the absence of unified fusion theories and methodologies for achieving effective fusion across diverse scenarios, as well as the need for enhanced fault tolerance and robustness. Additionally, there is a dearth of standardized benchmarks for evaluating fusion performance, as well as limited exploration into specific applications of multi-sensor image fusion.

This research endeavor is dedicated to advancing the understanding and application of multi-sensor image fusion. It aims to present cutting-edge studies encompassing methodologies, assessments, and practical implementations. The overarching goal is to drive the evolution of multi-sensor image fusion techniques, with a focus on their utilization in medical image segmentation, biological analysis, and astronomical imaging.

Specifically, this research initiative seeks contributions in the form of unified fusion theories, robust fusion methodologies, innovative performance evaluation techniques, and real-world applications in classification, detection, and segmentation tasks. Fusion methods may draw upon traditional processing techniques or employ deep learning approaches. Evaluation efforts should explore the creation of benchmarks comprising datasets, objective metrics, and baseline methodologies.

Moreover, investigations into specific applications are encouraged, such as the fusion of images from different modalities using multi-sensor fusion techniques. The following topics are of particular interest:

  • Multi-sensor image registration;
  • High dynamic range imaging;
  • Multi-sensor/modality object detection and segmentation;
  • Machine learning/deep learning for multi-sensor image processing;
  • Multi-sensor image fusion datasets and benchmarks;
  • Objective evaluations of multi-sensor image fusion;
  • Multi-sensor image fusion in different fields;
  • Multi-sensor information monitoring and detection;
  • Multi-sensor information processing.

Through collaborative research and exploration, this initiative aims to propel advancements in multi-sensor image fusion, paving the way for its widespread application across diverse domains.

Dr. Guanqiu Qi
Dr. Zhiqin Zhu
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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-sensor fusion
  • image processing
  • multi-sensor image segmentation
  • machine/deep learning
  • performance evaluation

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Published Papers (1 paper)

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36 pages, 5771 KB  
Article
Improving K-Means Clustering: A Comparative Study of Parallelized Version of Modified K-Means Algorithm for Clustering of Satellite Images
by Yuv Raj Pant, Larry Leigh and Juliana Fajardo Rueda
Algorithms 2025, 18(8), 532; https://doi.org/10.3390/a18080532 - 21 Aug 2025
Viewed by 1156
Abstract
Efficient clustering of high-spatial-dimensional satellite image datasets remains a critical challenge, particularly due to the computational demands of spectral distance calculations, random centroid initialization, and sensitivity to outliers in conventional K-Means algorithms. This study presents a comprehensive comparative analysis of eight parallelized variants [...] Read more.
Efficient clustering of high-spatial-dimensional satellite image datasets remains a critical challenge, particularly due to the computational demands of spectral distance calculations, random centroid initialization, and sensitivity to outliers in conventional K-Means algorithms. This study presents a comprehensive comparative analysis of eight parallelized variants of the K-Means algorithm, designed to enhance clustering efficiency and reduce computational burden for large-scale satellite image analysis. The proposed parallelized implementations incorporate optimized centroid initialization for better starting point selection using a dynamic K-Means sharp method to detect the outlier to improve cluster robustness, and a Nearest-Neighbor Iteration Calculation Reduction method to minimize redundant computations. These enhancements were applied to a test set of 114 global land cover data cubes, each comprising high-dimensional satellite images of size 3712 × 3712 × 16 and executed on multi-core CPU architecture to leverage extensive parallel processing capabilities. Performance was evaluated across three criteria: convergence speed (iterations), computational efficiency (execution time), and clustering accuracy (RMSE). The Parallelized Enhanced K-Means (PEKM) method achieved the fastest convergence at 234 iterations and the lowest execution time of 4230 h, while maintaining consistent RMSE values (0.0136) across all algorithm variants. These results demonstrate that targeted algorithmic optimizations, combined with effective parallelization strategies, can improve the practicality of K-Means clustering for high-dimensional-satellites image analysis. This work underscores the potential of improving K-Means clustering frameworks beyond hardware acceleration alone, offering scalable solutions good for large-scale unsupervised image classification tasks. Full article
(This article belongs to the Special Issue Algorithms in Multi-Sensor Imaging and Fusion)
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