Remote Sensing

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Prof. Dr. Seongwook Lee

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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

Published Papers (1 paper)

Research

17 pages, 26825 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/rs16091522 - 25 Apr 2024

Viewed by 297

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 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

x y

y z

, and

z x

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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