Satellite image scene classification using spatial information

Weiwei Song; Dunwei Wen; Ke Wang; Tong Liu; Mujun Zang

doi:10.1117/12.2178739

4 March 2015 Satellite image scene classification using spatial information

Weiwei Song, Dunwei Wen, Ke Wang, Tong Liu, Mujun Zang

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94431K (2015) https://doi.org/10.1117/12.2178739
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

Abstract

In order to enhance the local feature’s describing capacity and improve the classification performance of high-resolution (HR) satellite images, we present an HR satellite image scene classification method that make use of spatial information of local feature. First, the spatial pyramid matching model (SPMM) is adopted to encode spatial information of local feature. Then, images are represented by the local feature descriptors and encoding information. Finally, the support vector machine (SVM) classifier is employed to classify image scenes. The experiment results on a real satellite image dataset show that our method can classify the scene classes with an 82.6% accuracy, which indicates that the method can work well on describing HR satellite images and classifying different scenes.

Citation Download Citation

Weiwei Song, Dunwei Wen, Ke Wang, Tong Liu, and Mujun Zang "Satellite image scene classification using spatial information", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94431K (4 March 2015); https://doi.org/10.1117/12.2178739

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