Chinese Journal of Quantum Electronics, Volume. 32, Issue 5, 539(2015)
Design of improved hyperspectral image classification scheme based on weighted fuzzy C means algorithm
In order to improve the classification accuracy of hyperspectral image data, reduce dependence on large number of data sets, an improved method was proposed for feature extraction of hyperspectral data based on the weighted fuzzy C means algorithm. The approach is an extension of previous approach-prototype space feature extraction. Each feature with different weights in terms of weighted fuzzy C means algorithm to ensure the features contain more information after extracted. Experiment results show that compared to results obtained from approach prototype spatial feature extraction method, this method has a stability of data set and higher classification accuracy when extracted a small number of features, which greatly reduces the dependence on the number of data sets of samples, and improves the efficiency of the prototype spatial characteristics method.
Get Citation
Copy Citation Text
MA Huan, JING Zhiyong, CHEN Ming, ZHANG Jianwei. Design of improved hyperspectral image classification scheme based on weighted fuzzy C means algorithm[J]. Chinese Journal of Quantum Electronics, 2015, 32(5): 539
Category:
Received: Feb. 6, 2015
Accepted: --
Published Online: Oct. 22, 2015
The Author Email: Huan MA (mahy-aa@163.com)