Analysis of the fluence of multi-sources DEM in remote sensing image ortho-rectification

Rui Liu; Jingxing Zhu; Niangang Jiao; Feng Wang; Hongjian You; Yuming Xiang

doi:10.1117/12.3029617

21 June 2024 Analysis of the fluence of multi-sources DEM in remote sensing image ortho-rectification

Rui Liu, Jingxing Zhu, Niangang Jiao, Feng Wang, Hongjian You, Yuming Xiang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 1316705 (2024) https://doi.org/10.1117/12.3029617
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Digital Elevation Models (DEMs) depict the configuration of the Earth surface, which is essential for remote sensing image ortho-rectification. Nowadays, the ALOS DEM, ASTER GDEM and SRTM DEM are the most commonly used models, produced based on different remote sensing datasets. Geolocation errors may be introduced with different producing methods and datasets. Therefore, multi-sources remote sensing images covering various terrain in China are experimented for the analysis of ortho-rectification accuracy using different DEMs. Based on the rational function model, the geolocation error of original imagery can be reduced greatly with the grounds control points, and different DEMs are used for the ortho-rectification of different imagery. The accuracy is experimented and verified using high-precision reference images. Experimental results show that ortho-rectificated images with the ALOS DEM can achieve better accuracy compared with the SRTM DEM and ASTER GDEM, which is significant for geometric processing of remote sensing images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rui Liu, Jingxing Zhu, Niangang Jiao, Feng Wang, Hongjian You, and Yuming Xiang "Analysis of the fluence of multi-sources DEM in remote sensing image ortho-rectification", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 1316705 (21 June 2024); https://doi.org/10.1117/12.3029617

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available