Name
Title | ||
|---|---|---|
Abundance-Indicated Subspace for Hyperspectral Classification With Limited Training Samples. |
Abstract | ||
|---|---|---|
The imbalance between the (often limited) number of available training samples and the high data dimensionality, together with the presence of mixed pixels, often complicates the classification of remotely sensed hyperspectral data. In this paper, we tackle these problems by developing a new method that combines spectral unmixing and classification techniques in a subspace-based approach. The prop... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSTARS.2019.2903940 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Hyperspectral imaging,Training,Earth,Logistics,Feature extraction | Computer vision,Pattern recognition,Subspace topology,Multinomial logistic regression,Hyperspectral imaging,Linear subspace,Curse of dimensionality,Pixel,Artificial intelligence,Land cover,Spectral signature,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 4 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuyuan Xu | 1 | 0 | 0.68 |
| Jun Li | 2 | 1360 | 97.59 |
| Mahdi Khodadadzadeh | 3 | 68 | 9.12 |
| Andrea Marinoni | 4 | 48 | 13.37 |
| Paolo Gamba | 5 | 682 | 92.97 |
| Baochun Li | 6 | 9416 | 614.20 |