ASTEROID SHAPE RECONSTRUCTION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK

Chen Yan; Zehua Dong; Na Zhang; Zegang Ding

doi:10.1049/icp.2024.1778

ASTEROID SHAPE RECONSTRUCTION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK

Chen Yan, Zehua Dong^*, Na Zhang, Zegang Ding

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

Radar is the most powerful remote sensing tool for measuring the physical characteristics of near-Earth objects. In this paper, we propose an asteroid shape reconstruction method using convolutional neural networks, which can reconstruct the three-dimensional shape features of asteroids based on radar observation data. Compared to traditional methods, it has the advantages of automatic optimization and minimal manual intervention. Experimental results have been conducted to demonstrate the effectiveness of this method.

Original language	English
Pages (from-to)	4142-4145
Number of pages	4
Journal	IET Conference Proceedings
Volume	2023
Issue number	47
DOIs	https://doi.org/10.1049/icp.2024.1778
Publication status	Published - 2023
Event	IET International Radar Conference 2023, IRC 2023 - Chongqing, China Duration: 3 Dec 2023 → 5 Dec 2023

Keywords

ASTEROID SHAPE RECONSTRUCTION
DELAY-DOPPLER IMAGES
NEURAL NETWORK

Access to Document

10.1049/icp.2024.1778

Cite this

@article{9d62be834d82450280e47919635e4f78,

title = "ASTEROID SHAPE RECONSTRUCTION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK",

abstract = "Radar is the most powerful remote sensing tool for measuring the physical characteristics of near-Earth objects. In this paper, we propose an asteroid shape reconstruction method using convolutional neural networks, which can reconstruct the three-dimensional shape features of asteroids based on radar observation data. Compared to traditional methods, it has the advantages of automatic optimization and minimal manual intervention. Experimental results have been conducted to demonstrate the effectiveness of this method.",

keywords = "ASTEROID SHAPE RECONSTRUCTION, DELAY-DOPPLER IMAGES, NEURAL NETWORK",

author = "Chen Yan and Zehua Dong and Na Zhang and Zegang Ding",

note = "Publisher Copyright: {\textcopyright} The Institution of Engineering & Technology 2023.; IET International Radar Conference 2023, IRC 2023 ; Conference date: 03-12-2023 Through 05-12-2023",

year = "2023",

doi = "10.1049/icp.2024.1778",

language = "English",

volume = "2023",

pages = "4142--4145",

journal = "IET Conference Proceedings",

issn = "2732-4494",

publisher = "Institution of Engineering and Technology",

number = "47",

}

TY - JOUR

T1 - ASTEROID SHAPE RECONSTRUCTION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK

AU - Yan, Chen

AU - Dong, Zehua

AU - Zhang, Na

AU - Ding, Zegang

N1 - Publisher Copyright: © The Institution of Engineering & Technology 2023.

PY - 2023

Y1 - 2023

N2 - Radar is the most powerful remote sensing tool for measuring the physical characteristics of near-Earth objects. In this paper, we propose an asteroid shape reconstruction method using convolutional neural networks, which can reconstruct the three-dimensional shape features of asteroids based on radar observation data. Compared to traditional methods, it has the advantages of automatic optimization and minimal manual intervention. Experimental results have been conducted to demonstrate the effectiveness of this method.

AB - Radar is the most powerful remote sensing tool for measuring the physical characteristics of near-Earth objects. In this paper, we propose an asteroid shape reconstruction method using convolutional neural networks, which can reconstruct the three-dimensional shape features of asteroids based on radar observation data. Compared to traditional methods, it has the advantages of automatic optimization and minimal manual intervention. Experimental results have been conducted to demonstrate the effectiveness of this method.

KW - ASTEROID SHAPE RECONSTRUCTION

KW - DELAY-DOPPLER IMAGES

KW - NEURAL NETWORK

UR - http://www.scopus.com/inward/record.url?scp=85203177777&partnerID=8YFLogxK

U2 - 10.1049/icp.2024.1778

DO - 10.1049/icp.2024.1778

M3 - Conference article

AN - SCOPUS:85203177777

SN - 2732-4494

VL - 2023

SP - 4142

EP - 4145

JO - IET Conference Proceedings

JF - IET Conference Proceedings

IS - 47

T2 - IET International Radar Conference 2023, IRC 2023

Y2 - 3 December 2023 through 5 December 2023

ER -

ASTEROID SHAPE RECONSTRUCTION METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this