Name
Abstract | ||
|---|---|---|
Hyperspectral image classification is a challenging task when only a small number of labeled samples are available due to the difficult, expensive, and time-consuming ground campaigns required to collect the ground-truth information. It is also known that the classification performance is highly dependent on the size of the labeled data. In this letter, a semisupervised learning-based hyperspectra... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2017.2665679 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Training,Hyperspectral imaging,Support vector machines,Clustering algorithms,Training data,Probability | Small number,Kernel (linear algebra),Computer vision,Pattern recognition,Support vector machine,Sparse approximation,Hyperspectral imaging,Artificial intelligence,Pixel,Cluster analysis,Sample size determination,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 5 | 1545-598X |
Citations | PageRank | References |
4 | 0.38 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aydemir, M.Said | 1 | 4 | 1.40 |
| Gökhan Bilgin | 2 | 62 | 13.18 |