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Applied Sciences
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Advanced Remote Sensing Technologies and Their Applications
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Advanced Remote Sensing Technologies and Their Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 2446

Special Issue Editors

Prof. Dr. Ke Wu

E-Mail Website
Guest Editor
School of Geophysics and Spatial Information, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing technology; image processing; remote sensing applications; geosciences
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Chen

E-Mail Website
Guest Editor
School of Geophysics and Spatial Information, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing information models; geohazard prediction; ecological environment monitoring; multimodal data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanjin Xu

E-Mail Website
Guest Editor
College of Natural Resources, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing geology (geological resources, geological disasters and environment); comprehensive resource exploration and quantitative prediction; multi-source remote sensing comprehensive technology application (hyperspectral, multi-spectral, microwave, thermal infrared); resource and environmental big data and intelligent algorithms

Special Issue Information

Dear Colleagues,

With the increase and progressive development of remote sensing satellites and airborne sensors, it has become possible to acquire different types of data, enabling us to analyze the characteristics of the Earth’s surface and distinguish geological formations and units. The combined use of advanced technologies, such as deep learning, which was inspired by brain neural science, can enable the automatic learning of high-level semantic features from remote sensing images, offering a more refined level of accuracy than earlier remote sensing technologies. Based on this background, this Special Issue addresses hyper-spectral/multi-spectral image classification, unmixing, image fusion and sharpening, artificial intelligence and machine learning, lithological mapping, and other geological applications related to remote sensing.

Prof. Dr. Ke Wu
Dr. Tao Chen
Dr. Yuanjin Xu
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. Applied Sciences 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 2400 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

  • lithological mapping
  • deep learning
  • remote sensing imagery processing
  • image classification
  • image fusion and sharpening
  • artificial neural network
  • geological remote sensing applications

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Published Papers (2 papers)

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Research

18 pages, 20822 KiB  
Article
DSpix2pix: A New Dual-Style Controlled Reconstruction Network for Remote Sensing Image Super-Resolution
by Zhouyi Wang and Changcheng Wang
Appl. Sci. 2025, 15(3), 1179; https://doi.org/10.3390/app15031179 - 24 Jan 2025
Cited by 1 | Viewed by 759
Abstract
Super-resolution reconstruction is a critical task in remote sensing image classification, and generative adversarial networks (GANs) have emerged as a dominant approach in this field. Traditional generative networks often produce low-quality images at resolutions like 256 × 256, and current research on single-image [...] Read more.
Super-resolution reconstruction is a critical task in remote sensing image classification, and generative adversarial networks (GANs) have emerged as a dominant approach in this field. Traditional generative networks often produce low-quality images at resolutions like 256 × 256, and current research on single-image super-resolution typically focuses on resolution enhancement factors of two to four (2×–4×), which do not meet practical application demands. Building upon the framework of StyleGAN, this study introduces a dual-style controlled super-resolution reconstruction network referred to as DSpix2pix. It uses a fixed style vector (Style 1) from StyleGAN-v2, generated through its mapping network and applied to each layer in the generator. And an additional style vector (Style 2) is extracted from example images and injected into the decoder using AdIn, enhancing the balance of styles in the generated images. DSpix2pix is capable of generating high-quality, smoother, noise-reduced, and more realistic super-resolution remote sensing images at 512 × 512 and 1024 × 1024 resolutions. In terms of visual metrics such as RMSE, PSNR, SSIM, and LPIPS, it outperforms traditional super-resolution networks like SRGAN and UNIT, with RMSE consistently exceeding 10. The network excels in 2× and 4× super-resolution tasks, demonstrating potential for remote sensing image interpretation, and shows promising results in 8x super-resolution tasks. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Technologies and Their Applications)
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22 pages, 7056 KiB  
Article
Land Subsidence Predictions Based on a Multi-Component Temporal Convolutional Gated Recurrent Unit Model in Kunming City
by Tao Chen, Di Ning and Yuhang Liu
Appl. Sci. 2024, 14(21), 10021; https://doi.org/10.3390/app142110021 - 2 Nov 2024
Viewed by 1060
Abstract
Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional [...] Read more.
Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which integrates a fully adaptive noise-ensemble empirical-mode decomposition algorithm with a deep neural network to account for the complexity of time-series data. The model was validated using typical InSAR subsidence data from Kunming, analyzing the impact of each component on the prediction performance. A comparative analysis with the TCGRU model and models based on seasonal-trend decomposition using LOESS (STL) and empirical-mode decomposition (EMD) revealed that the MC-TCGRU model significantly enhanced the prediction accuracy by reducing the complexity of the original data. The model achieved R² values of 0.90, 0.93, 0.51, 0.93, and 0.96 across five points, outperforming the compared models. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Technologies and Their Applications)
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