Are ground-level visual attributes useful for high resolution remote sensing image classification?

Shuai Liu; Hao Sun; Shilin Zhou

doi:10.1117/12.2234916

2 March 2016 Are ground-level visual attributes useful for high resolution remote sensing image classification?

Shuai Liu, Hao Sun, Shilin Zhou

Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 990118 (2016) https://doi.org/10.1117/12.2234916
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China

Abstract

This paper extends the ground-level visual attributes to high resolution remote sensing imagery to demonstrate the useful-ness of visual attributes for remote sensing tasks such as image classification. Visual attributes have been introduced as the semantic properties that transcend the categories. We train predictors from the largest ground-level attributes datasets, SUN, for 102 visual attributes, which is well defined in SUN. We first form an attribute-based representation for the remote sensing imagery with the output of trained attribute predictors. We then evaluate the classification performances of the attribute-based representation against traditional features. Extensive experiments on the ground-level baseline dataset scene 15 and remote sensing dataset UCMLU shows that ground-level visual attributes outperform the traditional low-level features in the classification problem, and the combination of ground-level visual attribute and low-level features obtains best classification rate. Moreover, we demonstrate that attribute-based representation is much more semantically powerful than the low-level features.

Citation Download Citation

Shuai Liu, Hao Sun, and Shilin Zhou "Are ground-level visual attributes useful for high resolution remote sensing image classification?", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 990118 (2 March 2016); https://doi.org/10.1117/12.2234916

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Visualization

Remote sensing

Scene classification

Image classification

Image resolution

Classification systems

Sun

Show All Keywords

Keywords/Phrases

Search In:

Publication Years