Name
Abstract | ||
|---|---|---|
In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that driven by active learning, the performance of autoencoder can be greatly improved. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729116 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Autoencoder, hyperspectral imagery classification, active learning, deep learning | Computer vision,Autoencoder,Active learning,Pattern recognition,Computer science,Remote sensing,Search engine indexing,Hyperspectral imaging,Artificial intelligence,Classifier (linguistics),Artificial neural network | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yibao Sun | 1 | 0 | 0.34 |
| Jun Li | 2 | 1360 | 97.59 |
| Wei Wang | 3 | 3 | 1.09 |
| Antonio Plaza | 4 | 3475 | 262.63 |
| Zeqiang Chen | 5 | 84 | 14.00 |