Building detection based on saliency for high resolution satellite images

Ping Yang; Zhiguo Jiang; Hao Feng; Yibing Ma

doi:10.1117/12.2032154

26 October 2013 Building detection based on saliency for high resolution satellite images

Ping Yang, Zhiguo Jiang, Hao Feng, Yibing Ma

Author Affiliations +

Proceedings Volume 8918, MIPPR 2013: Automatic Target Recognition and Navigation; 89180D (2013) https://doi.org/10.1117/12.2032154
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

For building detection from single very high spatial resolution (VHR) satellite images, we take advantage of visual saliency and Bayesian model to rapidly locate roof-top areas. We firstly generate saliency map of an image by a salient contrast filter using low-level feature. This filter distinguishes salient pixels if a pixel is visually different from its surroundings in color or texture. Secondly, a Bayesian model is proposed to generate all closed rectangular contours as mid-level content in the image. We suggest the area enclosed by contour corresponds to high saliency values. Finally, the roof-top areas are extracted by fusing different level information mentioned above. Experimental results demonstrate the effectiveness of our algorithm.

Citation Download Citation

Ping Yang, Zhiguo Jiang, Hao Feng, and Yibing Ma "Building detection based on saliency for high resolution satellite images", Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 89180D (26 October 2013); https://doi.org/10.1117/12.2032154

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