Robust aircraft segmentation from very high-resolution images based on bottom-up and top-down cue integration

Feng Gao; Qizhi Xu; Bo Li

doi:10.1117/1.JRS.10.016003

19 January 2016 Robust aircraft segmentation from very high-resolution images based on bottom-up and top-down cue integration

Feng Gao, Qizhi Xu, Bo Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 1, 016003 (January 2016). https://doi.org/10.1117/1.JRS.10.016003

Abstract

Existing segmentation methods require manual interventions to optimally extract objects from cluttered background, so that they can hardly work well in automated surveillance systems. In order to automatically extract aircrafts from very high-resolution images, we proposed a segmentation method that combines bottom-up and top-down cues. Three essential principles from local contrast, global contrast, and center bias are involved to compute bottom-up cue. In addition, top-down cue is computed by incorporating aircraft shape priors, and it is achieved by training a classifier from a rich set of visual features. Iterative operations and adaptive fitting are designed to get refined results. Experimental results demonstrated that the proposed method can provide significant improvements on the segmentation accuracy.

Citation Download Citation

Feng Gao, Qizhi Xu, and Bo Li "Robust aircraft segmentation from very high-resolution images based on bottom-up and top-down cue integration," Journal of Applied Remote Sensing 10(1), 016003 (19 January 2016). https://doi.org/10.1117/1.JRS.10.016003

Published: 19 January 2016

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