Memetic Three-Dimensional Gabor Feature Extraction for Hyperspectral Imagery Classification
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- 作者：Zexuan Zhu (1)
Linlin Shen (1)
Yiwen Sun (2)
Shan He (3)
Zhen Ji (1) jizhen@szu.edu.cn - 关键词：Memetic Algorithm – ; Gabor Feature Extraction – ; Hyperspectral Imagery Classification
- 刊名：Lecture Notes in Computer Science
- 出版年：2012
- 出版时间：2012
- 年：2012
- 卷：7331
- 期：1
- 页码：479-488
- 全文大小：570.4 KB
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- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
文摘
This paper proposes a three-dimensional Gabor feature extraction for pixel-based hyperspectral imagery classification using a memetic algorithm. The proposed algorithm named MGFE combines 3-D Gabor wavelet feature generation and feature selection together to capture the signal variances of hyperspectral imagery, thereby extracting the discriminative 3-D Gabor features for accurate classification. MGFE is characterized with a novel fitness evaluation function based on independent feature relevance and a pruning local search for eliminating redundant features. The experimental results on two real-world hyperspectral imagery datasets show that MGFE succeeds in obtaining significantly improved classification accuracy with parsimonious feature selection.