By Topic

High-Resolution Remote-Sensing Image Classification via an Approximate Earth Mover's Distance-Based Bag-of-Features Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yasen Zhang ; Key Laboratory of Spatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing, China ; Xian Sun ; Hongqi Wang ; Kun Fu

High-resolution remote-sensing image classification is a challenging task. In this letter, we first propose a bag-of-features (BOF) model-based classification framework for high-resolution remote-sensing images via Earth mover's distance (EMD) to perform histogram matching. Compared with conventional BOF, EMD-based BOF is insensitive to vector quantization and can explore the relations among visual codes. In addition, such relations can be utilized as a key discriminative feature for image classification task. However, EMD is not practically utilized because of expensive computational cost. Motivated by Pele and Werman, we propose a faster approximate EMD (AEMD), and our AEMD-based BOF can inherit the advantages of EMD. Experimental results on a multicategory remote-sensing image data set demonstrate the effectiveness of our classification framework.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:10 ,  Issue: 5 )