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Advances in AI Technology for Remote Sensing Image Processing
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Advances in AI Technology for Remote Sensing Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 2991

Special Issue Editors

Dr. Yanlong Guo

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Guest Editor
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: natural resource management; AI technology; machine learning; remote sense; biodiversity
Dr. Wenfei Luan

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Guest Editor
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Interests: land cover classification; urban sustainability; deep learning; remote sensing
Dr. Zebin Zhao

E-Mail Website
Guest Editor
Heihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing; blind source separation; AI

Special Issue Information

Dear Colleagues,

This Special Issue highlights pioneering research that leverages advanced AI techniques—including deep learning, reinforcement learning, transfer learning, and long short-term memory (LSTM) networks—to transform the processing and analysis of remote sensing data. Emphasis is placed on the application of cutting-edge methods, such as causal analysis and physics-informed machine learning, in remote sensing. Additionally, the Special Issue will explore the development and advancements in knowledge graphs and large-scale foundational models, which are pivotal in enhancing the accuracy and interpretability of remote sensing analyses. These innovations are propelling the automation of remote sensing workflows, enabling efficient and scalable solutions. The Special Issue will also address critical technical challenges in making remote sensing data AI-READY, focusing on improving data quality, enhancing model interpretability, and optimizing computational efficiency. We especially welcome contributions that explore these themes, including the integration of the latest AI image processing techniques and their applications in fields such as environmental monitoring, urban planning, agriculture, and disaster management. By bringing together research from diverse disciplines, this Special Issue aims to provide a comprehensive overview of how AI is revolutionizing remote sensing, paving the way for more intelligent, automated, and reliable data processing solutions.

Dr. Yanlong Guo
Dr. Wenfei Luan
Dr. Zebin Zhao
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. Electronics 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

  • AI technology
  • remote sensing
  • deep learning
  • reinforcement learning
  • transfer learning
  • knowledge graphs
  • foundation model for remote sensing
  • AI-READY data

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Published Papers (4 papers)

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Research

19 pages, 3752 KiB  
Article
Feasibility Research on the Auxiliary Variables in Scaling of Soil Moisture Based on the SiB2 Model: A Case Study in Daman
by Zebin Zhao and Rui Jin
Electronics 2025, 14(7), 1392; https://doi.org/10.3390/electronics14071392 - 30 Mar 2025
Viewed by 63
Abstract
Soil moisture is a core climate variable in land surface processes and has a strong influence on the energy balance and water exchange between the land surface–vegetation–atmosphere columns. However, the low spatial resolution of soil moisture remote sensing products cannot satisfy the requirements [...] Read more.
Soil moisture is a core climate variable in land surface processes and has a strong influence on the energy balance and water exchange between the land surface–vegetation–atmosphere columns. However, the low spatial resolution of soil moisture remote sensing products cannot satisfy the requirements of research and applications based on hydro-meteorological and eco-hydrological simulations and the management of water resources at the watershed scale. A feasible solution is to downscale soil moisture products derived from microwave remote sensing, which often requires the support of auxiliary variables. Meanwhile, during the validation process of remote sensing products, the spatial scales between in situ observations and remote sensing pixel retrievals are inconsistent; thus, in situ observations should be translated to ground truths at a pixel scale via reasonable upscaling methods. Many auxiliary variables can serve as proxies in the scaling of soil moisture, although few studies have analyzed their feasibility and application conditions. In this paper, a SiB2 (Simple Biosphere Model-II) simulation for the Daman superstation from 1 May to 30 September 2013, was employed to calculate seven auxiliary variables related to soil moisture: ATIs and ATIc (Apparent Thermal Inertias based on surface soil temperature and canopy temperature), E (Evaporation), E/ETa (Ratio of Evaporation and Actual Evapotranspiration), E/ETp (Ratio of Evaporation and Potential Evapotranspiration), EF (Evaporative Fraction) and AEF (Actual Evaporative Fraction). The applicability of these variables was then evaluated via a correlation analysis between the variables and soil moisture. The results indicated that E is highly sensitive to soil moisture at Phase I (R2 ≥ 0.67), whereas ATIs is the greatest indicator of soil moisture at Phase II (R2 ≥ 0.51). Considering both the correlation and computability of these auxiliary variables, the EF (R2 ≥ 0.56) and AEF (R2 ≥ 0.54) are recommended as proxies for Phase I, while ATIs (R2 ≥ 0.51) is also recommended for Phase II. Full article
(This article belongs to the Special Issue Advances in AI Technology for Remote Sensing Image Processing)
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22 pages, 14296 KiB  
Article
Calibration-Enhanced Multi-Awareness Network for Joint Classification of Hyperspectral and LiDAR Data
by Quan Zhang, Zheyuan Cui, Tianhang Wang, Zhaoxin Li and Yifan Xia
Electronics 2025, 14(1), 102; https://doi.org/10.3390/electronics14010102 - 30 Dec 2024
Viewed by 538
Abstract
Hyperspectral image (HSI) and light detection and ranging (LiDAR) data joint classification has been applied in the field of ground category recognition. However, existing methods still perform poorly in extracting high-dimensional features and elevation information, resulting in insufficient data classification accuracy. To address [...] Read more.
Hyperspectral image (HSI) and light detection and ranging (LiDAR) data joint classification has been applied in the field of ground category recognition. However, existing methods still perform poorly in extracting high-dimensional features and elevation information, resulting in insufficient data classification accuracy. To address this challenge, we propose a novel and efficient Calibration-Enhanced Multi-Awareness Network (CEMA-Net), which exploits the joint spectral–spatial–elevation features in depth to realize the accurate identification of land cover categories. Specifically, we propose a novel multi-way feature retention (MFR) module that explores deep spectral–spatial–elevation semantic information in the data through multiple paths. In addition, we propose spectral–spatial-aware enhancement (SAE) and elevation-aware enhancement (EAE) modules, which effectively enhance the awareness of ground objects that are sensitive to spectral and elevation information. Furthermore, to address the significant representation disparities and spatial misalignments between multi-source features, we propose a spectral–spatial–elevation feature calibration fusion (SFCF) module to efficiently integrate complementary characteristics from heterogeneous features. It incorporates two key advantages: (1) efficient learning of discriminative features from multi-source data, and (2) adaptive calibration of spatial differences. Comparative experimental results on the MUUFL, Trento, and Augsburg datasets demonstrate that CEMA-Net outperforms existing state-of-the-art methods, achieving superior classification accuracy with better feature map precision and minimal noise. Full article
(This article belongs to the Special Issue Advances in AI Technology for Remote Sensing Image Processing)
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17 pages, 8145 KiB  
Article
Integrated Anti-Aliasing and Fully Shared Convolution for Small-Ship Detection in Synthetic Aperture Radar (SAR) Images
by Manman He, Junya Liu, Zhen Yang and Zhijian Yin
Electronics 2024, 13(22), 4540; https://doi.org/10.3390/electronics13224540 - 19 Nov 2024
Viewed by 741
Abstract
Synthetic Aperture Radar (SAR) imaging plays a vital role in maritime surveillance, yet the detection of small vessels poses a significant challenge when employing conventional Constant False Alarm Rate (CFAR) techniques, primarily due to the limitations in resolution and the presence of clutter. [...] Read more.
Synthetic Aperture Radar (SAR) imaging plays a vital role in maritime surveillance, yet the detection of small vessels poses a significant challenge when employing conventional Constant False Alarm Rate (CFAR) techniques, primarily due to the limitations in resolution and the presence of clutter. Deep learning (DL) offers a promising alternative, yet it still struggles with identifying small targets in complex SAR backgrounds because of feature ambiguity and noise. To address these challenges, our team has developed the AFSC network, which combines anti-aliasing techniques with fully shared convolutional layers to improve the detection of small targets in SAR imagery. The network is composed of three key components: the Backbone Feature Extraction Module (BFEM) for initial feature extraction, the Neck Feature Fusion Module (NFFM) for consolidating features, and the Head Detection Module (HDM) for final object detection. The BFEM serves as the principal feature extraction technique, with a primary emphasis on extracting features of small targets, The NFFM integrates an anti-aliasing element and is designed to accentuate the feature details of diminutive objects throughout the fusion procedure, HDM is the detection head module and adopts a new fully shared convolution strategy to make the model more lightweight. Our approach has shown better performance in terms of speed and accuracy for detecting small targets in SAR imagery, surpassing other leading methods on the SSDD dataset. It attained a mean Average Precision (AP) of 69.3% and a specific AP for small targets (APS) of 66.5%. Furthermore, the network’s robustness was confirmed using the HRSID dataset. Full article
(This article belongs to the Special Issue Advances in AI Technology for Remote Sensing Image Processing)
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27 pages, 9255 KiB  
Article
DDMAFN: A Progressive Dual-Domain Super-Resolution Network for Digital Elevation Model Based on Multi-Scale Feature Fusion
by Bing He, Xuebing Ma, Bo Kong, Bingchao Wang and Xiaoxue Wang
Electronics 2024, 13(20), 4078; https://doi.org/10.3390/electronics13204078 - 17 Oct 2024
Cited by 1 | Viewed by 1012
Abstract
This paper examines the multi-scale super-resolution challenge of digital elevation models in remote sensing. A dual-domain multi-scale attention fusion network is proposed, which reconstructs digital elevation image details step-by-step using cascading sub-networks. This model incorporates components like the wavelet guidance and separation module, [...] Read more.
This paper examines the multi-scale super-resolution challenge of digital elevation models in remote sensing. A dual-domain multi-scale attention fusion network is proposed, which reconstructs digital elevation image details step-by-step using cascading sub-networks. This model incorporates components like the wavelet guidance and separation module, multi-scale attention fusion blocks, dilated convolutional inception module, and edge enhancement module to improve feature extraction and fusion capabilities. A new loss function is designed to enhance the model’s robustness and stability. Experiments indicate that the proposed model outperforms 15 benchmark models in PSNR, RMSE, MAE, RMSEslope, and RMSEaspect metrics. In HMA data, The proposed model’s PSNR increases by 0.89 dB (~1.81%), and RMSE decreases by 1.22 m (~8.6%) compared to a state-of-the-art model. Compared to EDEM, which has the best elevation index, RMSEslope decreases by 0.79° (~16%). Additionally, the effectiveness and contribution of each DDMAFN component were verified through ablation experiments. Finally, on the SRTM dataset, The proposed model demonstrates superior performance even with interpolated degradation. Full article
(This article belongs to the Special Issue Advances in AI Technology for Remote Sensing Image Processing)
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