| Full text | |
| Author(s): |
Santos, A. C. S.
;
Pedrini, H.
Total Authors: 2
|
| Document type: | Journal article |
| Source: | International Journal of Remote Sensing; v. 37, n. 13, p. 3005-3020, 2016. |
| Web of Science Citations: | 3 |
| Abstract | |
Hyperspectral images usually have large volumes of data comprising hundreds of spectral bands. Removal of redundant bands can both reduce computational time and improve classification performance. This work proposes and analyses a band-selection method based on the k-means clustering strategy combined with a classification approach using entropy filtering. Experimental results in different terrain images show that our method can significantly reduce the number of bands while maintaining an accurate classification. (AU) | |
| FAPESP's process: | 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications |
| Grantee: | Luis Gustavo Nonato |
| Support Opportunities: | Research Projects - Thematic Grants |