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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 Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1705

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
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 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.

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. Remote Sensing 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 2700 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 polices can be found here.

Published Papers (2 papers)

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Research

19 pages, 5790 KiB  
Article
Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection
by Shigang Wang, Xiangyuan Zhang, Yifan Zhao, Haozi Yu and Bin Li
Remote Sens. 2024, 16(17), 3263; https://doi.org/10.3390/rs16173263 - 3 Sep 2024
Viewed by 451
Abstract
As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. [...] Read more.
As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Sensor Data Processing for Remote Sensing)
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17 pages, 26825 KiB  
Article
Efficient Target Classification Based on Vehicle Volume Estimation in High-Resolution Radar Systems
by Sanghyeok Hwangbo, Seonmin Cho, Junho Kim and Seongwook Lee
Remote Sens. 2024, 16(9), 1522; https://doi.org/10.3390/rs16091522 - 25 Apr 2024
Viewed by 833
Abstract
In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In [...] Read more.
In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Sensor Data Processing for Remote Sensing)
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