A CNN-based method for adaptive landmark selection in remote sensing image

Yongzhan Chen; Weidong Yang; Yaoxin Cao; Chenhua Liu; Yang Dong

doi:10.1117/12.2539454

14 February 2020 A CNN-based method for adaptive landmark selection in remote sensing image

Yongzhan Chen, Weidong Yang, Yaoxin Cao, Chenhua Liu, Yang Dong

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 114290Z (2020) https://doi.org/10.1117/12.2539454
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

The technology of automatic selecting landmark plays a significant role in aircraft navigation and ground information assurance. Compared to the normal object detection, it is quite difficult to describe and quantify the characteristics of a landmark due to its various status and no stable structure. This paper attempts to innovatively combine CNN with the technology of selecting landmark. The algorithm used in this paper uses a structurally stable adaptation region as a learning sample to train the CNN classification model. In the selection phase, remote sensing images were cut into pieces of patches, landmark of which was then recognized through the CNN classification model. Non-maxima suppression was used to filter out the low rate landmark and a correlation peak-based uniqueness analysis (the ratio of primary and secondary peaks and the highest sharpness of peak) was used to ensure landmark with no similarity pattern in the remote sensing image. The results indicate the effectiveness of proposed method for Selecting Remote Sensing Image Adaptation Structure.

Citation Download Citation

Yongzhan Chen, Weidong Yang, Yaoxin Cao, Chenhua Liu, and Yang Dong "A CNN-based method for adaptive landmark selection in remote sensing image", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290Z (14 February 2020); https://doi.org/10.1117/12.2539454

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