Height correction based on multiple subimage correlation

Rong Fan; Yuehuan Wang

doi:10.1117/12.2210890

14 December 2015 Height correction based on multiple subimage correlation

Rong Fan, Yuehuan Wang

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981314 (2015) https://doi.org/10.1117/12.2210890
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

In this paper, a height correction approach is proposed based on multiple sub-image correlation in remote sensing and navigation systems. First, multiple subareas are selected in the reference image, combination of two of which consists of the two for height correction. Then the measured distance and real distance between two areas can be computed by correlation matching and perspective transformation model. With the distances, height deviation can be estimated for further processing. Considering the accuracy-loss caused by image blurring, noise, changes of scales and so on, the approach utilizes clustering method to improve accuracy. Experiments show that the proposed method can estimate the height deviation automatically and the accuracy of the proposed method is equivalent to that based on manually labeled.

Citation Download Citation

Rong Fan and Yuehuan Wang "Height correction based on multiple subimage correlation", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981314 (14 December 2015); https://doi.org/10.1117/12.2210890

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