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Artificial Intelligence-Based Sensor Data Processing for Remote Sensing
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Artificial Intelligence-Based Sensor Data Processing for Remote Sensing

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 August 2025) | Viewed by 804

Special Issue Editors

Prof. Dr. Seongwook Lee

E-Mail Website
Guest Editor
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: artificial intelligence; radar signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Byung-Kwan Kim

E-Mail Website
Guest Editor
Department of Radio Science and Information Communication Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Interests: machine learning using radar signals; distributed radar system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue deals with the various artificial intelligence algorithms that can be used in remote sensing. In particular, it will cover signal and image processing techniques and sensor fusion systems for sensors widely used in remote sensing, such as cameras, lidar, and radar. It will also introduce artificial intelligence and deep learning-based methods for this purpose.

Including sensing in indoor and outdoor environments, this Special Issue will introduce research related to remote sensing in environments such as ground and space. It also aims to cover various artificial intelligence-based algorithms related to target detection, tracking, recognition, and identification techniques. Artificial intelligence algorithms can be applied in many areas of remote sensing, and studies on various datasets and experimental results will also be comprehensively covered.

Our suggested themes and article types for submissions including but not limited to:

  • Artificial intelligence/deep learning for remote sensing;
  • Sensors (e.g., camera, lidar, and radar) for remote sensing;
  • Fusion of heterogeneous sensor data;
  • Datasets for AI and deep learning;
  • AI-based signal/image processing for remote sensing.

You may choose our Joint Special Issue in Remote Sensing.

Prof. Dr. Seongwook Lee
Dr. Byung-Kwan Kim
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. Sensors 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

  • remote sensing
  • artificial intelligence/deep learning
  • sensors (e.g., camera, lidar, radar)
  • sensor fusion
  • signal/image processing
  • target detection and tracking
  • target recognition and classification
  • image segmentation

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

Published Papers (2 papers)

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Research

24 pages, 3401 KB  
Article
Enhanced Hyperspectral Image Classification Technique Using PCA-2D-CNN Algorithm and Null Spectrum Hyperpixel Features
by Haitao Liu, Weihong Bi and Neelam Mughees
Sensors 2025, 25(18), 5790; https://doi.org/10.3390/s25185790 - 17 Sep 2025
Viewed by 151
Abstract
With the increasing availability of high-dimensional hyperspectral data from modern remote sensing platforms, accurate and efficient classification methods are urgently needed to overcome challenges such as spectral redundancy, spatial variability, and the curse of dimensionality. The current hyperspectral image classification technique has become [...] Read more.
With the increasing availability of high-dimensional hyperspectral data from modern remote sensing platforms, accurate and efficient classification methods are urgently needed to overcome challenges such as spectral redundancy, spatial variability, and the curse of dimensionality. The current hyperspectral image classification technique has become a crucial tool for analyzing material information in images. However, traditional classification methods face limitations when dealing with multidimensional data. To address these challenges and optimize hyperspectral image classification algorithms, this study employs a novel fusion method that combines principal component analysis (PCA) based on null spectral information and 2D convolutional neural networks (CNNs). First, the original spectral data are downscaled using PCA to reduce redundant information and extract essential features. Next, 2D CNNs are applied to further extract spatial features and perform feature fusion. The powerful adaptive learning capabilities of CNNs enable effective classification of hyperspectral images by jointly processing spatial and spectral features. The findings reveal that the proposed algorithm achieved classification accuracies of 98.98% and 97.94% on the Pavia and Indian Pines datasets, respectively. Compared to traditional methods, such as support vector machines (SVMs) and extreme learning machines (ELMs), the proposed algorithm achieved competitive performance with 98.81% and 98.64% accuracy on the same datasets, respectively. This approach not only enhances the accuracy and efficiency of the hyperspectral image classification but also provides a promising solution for remote sensing data processing and analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Sensor Data Processing for Remote Sensing)
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19 pages, 4376 KB  
Article
A Quadrotor UAV Aeromagnetic Compensation Method Based on Time–Frequency Joint Representation Neural Network and Its Application in Mineral Exploration
by Ping Yu, Guanlin Huang, Jian Jiao, Longran Zhou, Yuzhuo Zhao, Pengyu Lu, Lu Li and Shuiyan Shi
Sensors 2025, 25(18), 5774; https://doi.org/10.3390/s25185774 - 16 Sep 2025
Viewed by 242
Abstract
Quadrotor UAV-based aeromagnetic survey for mineral exploration has become a crucial solution in modern airborne geophysics due to its prominent advantages of cost-effectiveness and high efficiency. During the detection process, the magnetic anomaly interference generated by the quadrotor UAV itself reduces the signal-to-noise [...] Read more.
Quadrotor UAV-based aeromagnetic survey for mineral exploration has become a crucial solution in modern airborne geophysics due to its prominent advantages of cost-effectiveness and high efficiency. During the detection process, the magnetic anomaly interference generated by the quadrotor UAV itself reduces the signal-to-noise ratio (SNR) of the target signal, and some noise overlaps with the target signal in both time and frequency domains. Traditional methods exhibit poor compensation capability for such noise. To address these issues, this paper proposes an aeromagnetic compensation method based on a time–frequency joint representation neural network. This method combines continuous wavelet transform (CWT) and bidirectional long short-term memory (Bi-LSTM) to establish a prediction model. It uses wavelet transform to extract the frequency variation characteristics of the UAV’s magnetic interference, and it inputs these frequency characteristics along with the original time-domain data into the Bi-LSTM network to predict the UAV’s noise. Bi-LSTM can effectively extract the temporal logical connections in time-series signals, thereby improving the accuracy of the compensation model and ensuring high robustness. In this study, magnetic interference data from quadrotor UAV compensation flights were collected for experiments to evaluate the performance of the proposed method. Experimental results show that the neural network fused with time–frequency features, when applied to UAV aeromagnetic compensation, significantly enhances the accuracy and robustness of the compensation method. To verify the method’s effectiveness in removing UAV-generated noise during actual exploration, aeromagnetic survey data from a specific area were compensated using this method. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Sensor Data Processing for Remote Sensing)
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