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Sensors 2016, 16(3), 392; doi:10.3390/s16030392

Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel Coding

1
High-Tech Institute of Xi’an, Xi’an 710025, China
2
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 26 October 2015 / Revised: 14 March 2016 / Accepted: 15 March 2016 / Published: 18 March 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [4478 KB, uploaded 18 March 2016]   |  

Abstract

Scene-level geographic image classification has been a very challenging problem and has become a research focus in recent years. This paper develops a supervised collaborative kernel coding method based on a covariance descriptor (covd) for scene-level geographic image classification. First, covd is introduced in the feature extraction process and, then, is transformed to a Euclidean feature by a supervised collaborative kernel coding model. Furthermore, we develop an iterative optimization framework to solve this model. Comprehensive evaluations on public high-resolution aerial image dataset and comparisons with state-of-the-art methods show the superiority and effectiveness of our approach. View Full-Text
Keywords: scene-level geographic image classification; covariance descriptor; collaborative kernel coding scene-level geographic image classification; covariance descriptor; collaborative kernel coding
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, C.; Liu, H.; Wang, S.; Liao, S. Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel Coding. Sensors 2016, 16, 392.

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