Name
Abstract | ||
|---|---|---|
Hyperspectral image (HSI) classification has been long envisioned in the remote sensing community. Many methods have been proposed for HSI classification. Among them, the method of fusing spatial features has been widely used and achieved good performance. Aiming at the problem of spatial feature extraction in spectral-spatial HSI classification, we proposed a guided filter-based method. We attempted two fusion methods for spectral and spatial features. In order to optimize the classification results, we also adopted a guided filter to obtain better results. We apply the support vector machine (SVM) to classify the HSI. Experiments show that our proposed methods can obtain very competitive results than compared methods on all the three popular datasets. More importantly, our methods are fast and easy to implement. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s13638-019-1346-z | EURASIP Journal on Wireless Communications and Networking |
Keywords | Field | DocType |
Support vector machine, Guided filter, Hyperspectral image classification | Hyperspectral image classification,Pattern recognition,Computer science,Support vector machine,Feature extraction,Real-time computing,Hyperspectral imaging,Artificial intelligence | Journal |
Volume | Issue | ISSN |
2019 | 1 | 1687-1499 |
Citations | PageRank | References |
2 | 0.38 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanhui Guo | 1 | 321 | 40.94 |
| Xijie Yin | 2 | 2 | 0.38 |
| Xuechen Zhao | 3 | 2 | 1.40 |
| Dongxin Yang | 4 | 2 | 0.38 |
| Yu Bai | 5 | 14 | 8.86 |