Name
Abstract | ||
|---|---|---|
The classification of hyperspectral and multimodal remote sensing data is affected by two key problems: the high dimensionality of the input data and the limited number of the labeled samples. In this letter, a multimetric learning approach that combines feature extraction and active learning (AL) is introduced to deal with these two issues simultaneously. In particular, distinct metrics are assig... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LGRS.2016.2560623 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Feature extraction,Measurement,Hidden Markov models,Hyperspectral imaging,Laser radar | Remote sensing,Lidar,Artificial intelligence,k-nearest neighbors algorithm,Computer vision,Feature vector,Active learning,Pattern recognition,Hyperspectral imaging,Curse of dimensionality,Feature extraction,Hidden Markov model,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 7 | 1545-598X |
Citations | PageRank | References |
2 | 0.36 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou Zhang | 1 | 10 | 3.18 |
| Edoardo Pasolli | 2 | 285 | 17.04 |
| hsiuhan lexie yang | 3 | 129 | 8.75 |
| Melba M. Crawford | 4 | 1311 | 83.56 |