Advanced Mathematical Methods in Remote Sensing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 716

Special Issue Editors


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Guest Editor
Department of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, Kaciceva 26, 10 000 Zagreb, Croatia
Interests: remote sensing; image classification; data fusion; multicriterial analysis; multispectral and hyperspectral images procession
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Guest Editor
Department of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
Interests: formal knowledge representation; automated reasoning; machine learning; information retrieval; semantic web
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Electrical Engineering, University of Sarajevo, Zmaja od Bosne 8, BH-71000 Sarajevo, Bosnia and Herzegovina
Interests: artificial intelligence; machine learning; deep learning; digital signal processing; design and synthesis of digital systems; computer architectures; logic design; embedded computing

Special Issue Information

Dear Colleagues,

Remote sensing is part of Earth observation and is now used in all spheres of human society to monitor and quickly obtain a large amount of data over a large area (phenomena and objects in this area). With the technological development of humankind, computers are also becoming more advanced, and their use is increasing. These advances make it possible to perform more complex mathematical operations, such as processing multispectral and hyperspectral images (a larger number of images of the same area taken at different wavelengths) using machine learning methods and, in particular, deep learning. Advances in computer technology have led to the rapid development of artificial intelligence methods and their application in many areas of human activity. Today, artificial intelligence can be implemented in the cloud computing environment, offering more flexibility, agility, and cost savings by hosting data and applications. Very sophisticated and large-scale data processing tasks can be performed remotely without the need for specialized and expensive mainframe computers. Affordable compact industrial computers are quite sufficient to perform the complex mathematical operations required.

Mathematical methods and algorithms are the basis of many conventional and modern tools used in remote sensing, especially digital image processing and spatial data analysis and processing.

The goal of this Special Issue is to attract and publish manuscripts that present advanced developed mathematical models and algorithms that are applied within remote sensing methods. This mainly refers to the processing of digital images, sensor fusion, big data analysis, and the visualization of the results of remote sensing methods. Contributions on modern uses of mathematical methods and an algorithm for processing a large number of images and data are especially invited. This refers mostly to machine and deep learning methods in image processing and big data and artificial intelligence methods in remote sensing.

Dr. Andrija Krtalić
Dr. Marko Horvat
Dr. Amila Akagic
Guest Editors

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Keywords

  • digital image processing
  • big data analysis and processing
  • image classification (multispectral, hyperspectral)
  • spatial data analysis
  • spatiotemporal monitoring and analyzing
  • data visualization and presentation
  • machine learning
  • artificial neural networks and deep learning
  • artificial intelligence methods.

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Published Papers (1 paper)

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Research

13 pages, 11855 KiB  
Article
SSA-GAN: Singular Spectrum Analysis-Enhanced Generative Adversarial Network for Multispectral Pansharpening
by Lanfa Liu, Jinian Zhang, Baitao Zhou, Peilun Lyu and Zhanchuan Cai
Mathematics 2025, 13(5), 745; https://doi.org/10.3390/math13050745 - 25 Feb 2025
Viewed by 511
Abstract
Pansharpening is essential for remote sensing applications requiring high spatial and spectral resolution. In this paper, we propose a novel Singular Spectrum Analysis-Enhanced Generative Adversarial Network (SSA-GAN) for multispectral pansharpening. We designed SSA modules within the generator, enabling more effective extraction and utilization [...] Read more.
Pansharpening is essential for remote sensing applications requiring high spatial and spectral resolution. In this paper, we propose a novel Singular Spectrum Analysis-Enhanced Generative Adversarial Network (SSA-GAN) for multispectral pansharpening. We designed SSA modules within the generator, enabling more effective extraction and utilization of spectral features. Additionally, we introduce Pareto optimization to the nonreference loss function to improve the overall performance. We conducted comparative experiments on two representative datasets, QuickBird and Gaofen-2 (GF-2). On the GF-2 dataset, the Peak Signal-to-Noise Ratio (PSNR) reached 30.045 and Quality with No Reference (QNR) achieved 0.920, while on the QuickBird dataset, PSNR and QNR were 24.262 and 0.817, respectively. These results indicate that the proposed method can generate high-quality pansharpened images with enhanced spatial and spectral resolution. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Remote Sensing)
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