Name
Abstract | ||
|---|---|---|
Although it is a powerful feature selection algorithm, the wrapper method is rarely used for hyperspectral band selection. Its accuracy is restricted by the number of labeled training samples and collecting such label information for hyperspectral image is time consuming and expensive. Benefited from the local smoothness of hyperspectral images, a simple yet effective semisupervised wrapper method... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2017.2755541 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Feature extraction,Hyperspectral imaging,Image edge detection,Support vector machines,Training,Image color analysis | Data mining,Data set,Feature selection,Computer science,Artificial intelligence,Smoothness,Computer vision,Band selection,Pattern recognition,Support vector machine,Filter (signal processing),Feature extraction,Hyperspectral imaging | Journal |
Volume | Issue | ISSN |
14 | 11 | 1545-598X |
Citations | PageRank | References |
6 | 0.42 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianghai Cao | 1 | 27 | 2.49 |
| Cuicui Wei | 2 | 6 | 0.42 |
| Jungong Han | 3 | 1785 | 117.64 |
| Licheng Jiao | 4 | 5698 | 475.84 |