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Proceedings Paper

A CNN-based method for adaptive landmark selection in remote sensing image
Author(s): Yongzhan Chen; Weidong Yang; Yaoxin Cao; Chenhua Liu; Yang Dong
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Paper 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.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290Z (14 February 2020); doi: 10.1117/12.2539454
Show Author Affiliations
Yongzhan Chen, Huazhong Univ. of Science and Technology (China)
Weidong Yang, Huazhong Univ. of Science and Technology (China)
Yaoxin Cao, Shanghai Aerospace Control Technology Institute (China)
Chenhua Liu, Shanghai Aerospace Control Technology Institute (China)
Yang Dong, Shanghai Aerospace Control Technology Institute (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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