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Mathematics
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Computing in Image Processing for Remote Sensing and Biomedical Applications
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Computing in Image Processing for Remote Sensing and Biomedical Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1778

Special Issue Editors

Prof. Dr. Volodymyr Ponomaryov

E-Mail Website
Guest Editor
ESIME-Culhuacan, Instituto Politecnico Nacional, Mexico City 04330, Mexico
Interests: image/video filtering; medical imaging; super-resolution; machine/deep learning in biomedicine; 3D visualization
Prof. Dr. Vladimir Lukin

E-Mail Website
Guest Editor
Department of Information-Communication Technologies, National Aerospace University, Chkalova Str., 61070 Kharkov, Ukraine
Interests: image filtering in remote sensing applications; image compression; image filtering, medical imaging; image quality metrics; machine learning in biomedicine
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bogdan Smolka

E-Mail Website
Guest Editor
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: image denoising; image segmentation; image super-resolution; object detection; deep learning-based filtering
Special Issues, Collections and Topics in MDPI journals
Dr. Beatriz P. García Salgado

E-Mail Website
Guest Editor
ESIME-Culhuacan, Instituto Politecnico Nacional, Mexico City 04330, Mexico
Interests: hyperspectral imaging; medical imaging; image classification; image super-resolution; machine/deep learning in images; parallel computing

Special Issue Information

Dear Colleagues,

Image processing and computer vision are both extensive fields that continue to impact and bring innovation to human civilization, with application areas including fundamental research in remote sensing, biomedical imaging, security, personal identification, biometrics, and so forth.

This Special Issue aims to gather a collection of articles reflecting the latest developments in computing methods in image processing for remote sensing and biomedical applications. We invite authors to contribute original research and survey manuscripts addressing significant issues and contributing to the development of new concepts.

Articles could address the theory behind optimization and computing algorithms, as well as their specific applications in problem areas such as image compression, the removal of different kinds of noises, restoration in image processing, image segmentation, image/video super-resolution, parameter tuning, classification techniques in pattern recognition environments, 3D visualization, data analysis, and feature extraction for remote sensing and biomedical applications, aiming to enhance the efficiency and effectiveness of various image processing tasks.

Prof. Dr. Volodymyr Ponomaryov
Prof. Dr. Vladimir Lukin
Prof. Dr. Bogdan Smolka
Dr. Beatriz P. García Salgado
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. Mathematics 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

  • image registration
  • image/video reconstruction
  • image/video compression
  • image analysis
  • image processing
  • image segmentation and classification
  • image/video super-resolution in remote sensing
  • computer vision
  • 3D visualization
  • machine/deep learning in remote sensing and medical imaging
  • parallel computing in imaging
  • computational models
  • medical imaging and data analysis

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

Published Papers (3 papers)

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25 pages, 15203 KiB  
Article
Static-Aperture Synthesis Method in Remote Sensing and Non-Destructive Testing Applications
by Olha Inkarbaieva, Denys Kolesnikov, Danyil Kovalchuk, Volodymyr Pavlikov, Volodymyr Ponomaryov, Beatriz Garcia-Salgado, Valerii Volosyuk and Semen Zhyla
Mathematics 2025, 13(3), 502; https://doi.org/10.3390/math13030502 - 3 Feb 2025
Viewed by 157
Abstract
The study is dedicated to the statistical optimization of radar imaging of surfaces with the synthetic aperture radar (SAR) technique, assuming a static surface area and applying the ability to move a sensor along a nonlinear trajectory via developing a new method and [...] Read more.
The study is dedicated to the statistical optimization of radar imaging of surfaces with the synthetic aperture radar (SAR) technique, assuming a static surface area and applying the ability to move a sensor along a nonlinear trajectory via developing a new method and validating its operability for remote sensing and non-destructive testing. The developed models address the sensing geometry for signals reflected from a surface along with the observation signal–noise equation, including correlation properties. Moreover, the optimal procedures for coherent radar imaging of surfaces with the static SAR technology are synthesized according to the maximum likelihood estimation (MLE). The features of the synthesized algorithm are the decoherence of the received oscillations, the matched filtering of the received signals, and the possibility of using continuous signal coherence. Furthermore, the developed optimal and quasi-optimal algorithms derived from the proposed MLE have been investigated. The novel framework for radio imaging has demonstrated good overall operability and efficiency during simulation modeling (using the MATLAB environment) for real sensing scenes. The developed algorithms of spatio–temporal signal processing in systems with a synthesized antenna with nonlinear carrier trajectories open a promising direction for creating new methods of high-precision radio imaging from UAVs and helicopters. Full article
(This article belongs to the Special Issue Computing in Image Processing for Remote Sensing and Biomedical Applications)
13 pages, 20306 KiB  
Article
Clustering-Based Class Hierarchy Modeling for Semantic Segmentation Using Remotely Sensed Imagery
by Lanfa Liu, Song Wang, Zichen Tong and Zhanchuan Cai
Mathematics 2025, 13(3), 331; https://doi.org/10.3390/math13030331 - 21 Jan 2025
Viewed by 360
Abstract
Land use/land cover (LULC) nomenclature is commonly organized as a tree-like hierarchy, contributing to hierarchical LULC mapping. The hierarchical structure is typically defined by considering natural characteristics or human activities, which may not optimally align with the discriminative features and class relationships present [...] Read more.
Land use/land cover (LULC) nomenclature is commonly organized as a tree-like hierarchy, contributing to hierarchical LULC mapping. The hierarchical structure is typically defined by considering natural characteristics or human activities, which may not optimally align with the discriminative features and class relationships present in remotely sensed imagery. This paper explores a novel cluster-based class hierarchy modeling framework that generates data-driven hierarchical structures for LULC semantic segmentation. First, we perform spectral clustering on confusion matrices generated by a flat model, and then we introduce a hierarchical cluster validity index to obtain the optimal number of clusters to generate initial class hierarchies. We further employ ensemble clustering techniques to yield a refined final class hierarchy. Finally, we conduct comparative experiments on three benchmark datasets. Results demonstrating that the proposed method outperforms predefined hierarchies in both hierarchical LULC segmentation and classification. Full article
(This article belongs to the Special Issue Computing in Image Processing for Remote Sensing and Biomedical Applications)
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14 pages, 14439 KiB  
Article
Class-Aware Self- and Cross-Attention Network for Few-Shot Semantic Segmentation of Remote Sensing Images
by Guozhen Liang, Fengxi Xie and Ying-Ren Chien
Mathematics 2024, 12(17), 2761; https://doi.org/10.3390/math12172761 - 6 Sep 2024
Viewed by 886
Abstract
Few-Shot Semantic Segmentation (FSS) has drawn massive attention recently due to its remarkable ability to segment novel-class objects given only a handful of support samples. However, current FSS methods mainly focus on natural images and pay little attention to more practical and challenging [...] Read more.
Few-Shot Semantic Segmentation (FSS) has drawn massive attention recently due to its remarkable ability to segment novel-class objects given only a handful of support samples. However, current FSS methods mainly focus on natural images and pay little attention to more practical and challenging scenarios, e.g., remote sensing image segmentation. In the field of remote sensing image analysis, the characteristics of remote sensing images, like complex backgrounds and tiny foreground objects, make novel-class segmentation challenging. To cope with these obstacles, we propose a Class-Aware Self- and Cross-Attention Network (CSCANet) for FSS in remote sensing imagery, consisting of a lightweight self-attention module and a supervised prior-guided cross-attention module. Concretely, the self-attention module abstracts robust unseen-class information from support features, while the cross-attention module generates a superior quality query attention map for directing the network to focus on novel objects. Experiments demonstrate that our CSCANet achieves outstanding performance on the standard remote sensing FSS benchmark iSAID-5i, surpassing the existing state-of-the-art FSS models across all combinations of backbone networks and K-shot settings. Full article
(This article belongs to the Special Issue Computing in Image Processing for Remote Sensing and Biomedical Applications)
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