Name
Title | ||
|---|---|---|
Hyperspectral Image Classification Based On Empirical Mode Decomposition And Local Binary Pattern |
Abstract | ||
|---|---|---|
Traditional hyperspectral image classification methods always focused on spectral information, and lots of spatial information was neglected. Therefore, this paper introduces the spatial texture information in the process of hyperspectral image classification, and focuses on how to deeply combine the texture information and the spectral information. Based on empirical mode decomposition and local binary pattern, the method of support vector machine is used to classify hyperspectral image, in order to improve the image classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67777-4_39 | INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, ISCIDE 2017 |
Keywords | Field | DocType |
Hyperspectral image classification, Empirical mode decomposition, Local binary patterns, Support vector machine | Spatial analysis,Hyperspectral image classification,Computer vision,Pattern recognition,Computer science,Support vector machine,Local binary patterns,Hyperspectral imaging,Artificial intelligence,Contextual image classification,Hilbert–Huang transform | Conference |
Volume | ISSN | Citations |
10559 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changli Li | 1 | 0 | 1.35 |
| Hang Zuo | 2 | 0 | 0.34 |
| Xin Wang | 3 | 5 | 4.17 |
| Aiye Shi | 4 | 0 | 0.34 |
| Tanghuai Fan | 5 | 13 | 9.73 |