Name
Abstract | ||
|---|---|---|
Classification of remotely sensed hyperspectral images via supervised approaches is typically affected by high dimensionality of the spectral data and a limited number of labeled samples. Dimensionality reduction via feature extraction and active learning (AL) are two approaches that researchers have investigated independently to deal with these two problems. In this paper, we propose a new method... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TGRS.2015.2490482 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Training,Feature extraction,Hyperspectral imaging,Measurement,Optimization | k-nearest neighbors algorithm,Computer vision,Data set,Feature vector,Dimensionality reduction,Pattern recognition,Hyperspectral imaging,Feature extraction,Curse of dimensionality,Artificial intelligence,Large margin nearest neighbor,Mathematics | Journal |
Volume | Issue | ISSN |
54 | 4 | 0196-2892 |
Citations | PageRank | References |
7 | 0.48 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edoardo Pasolli | 1 | 285 | 17.04 |
| hsiuhan lexie yang | 2 | 129 | 8.75 |
| Melba M. Crawford | 3 | 1311 | 83.56 |