Name
Title | ||
|---|---|---|
Unsupervised Bayesian Classification of a Hyperspectral Image Based on the Spectral Mixture Model and Markov Random Field. |
Abstract | ||
|---|---|---|
Typical unsupervised classification of hyperspectral imagery (HSI) uses a Gaussian mixture model to determine intensity similarity of pixels. However, the existence of mixed pixels in HSI tends to reduce the effectiveness of the similarity measure and leads to large classification errors. Since a semantic class is always dominated by a particular endmember, a mixed pixel can be better classified b... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTARS.2018.2858008 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Bayes methods,Mixture models,Data models,Hyperspectral imaging,Markov processes | Endmember,Computer vision,Naive Bayes classifier,Similarity measure,Pattern recognition,Markov random field,Pixel,Artificial intelligence,Maximum a posteriori estimation,Discriminative model,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
11 | 9 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Fang | 1 | 0 | 3.38 |
| Xu, L. | 2 | 46 | 7.46 |
| Junhuan Peng | 3 | 26 | 9.66 |
| Honglei Yang | 4 | 1 | 1.71 |
| Alexander Wong | 5 | 351 | 69.61 |
| David A. Clausi | 6 | 1082 | 89.57 |