Name
Title | ||
|---|---|---|
Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network. |
Abstract | ||
|---|---|---|
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strate... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2799324 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Feature extraction,Task analysis,Machine learning,Hyperspectral imaging,Bayes methods,Data mining | Spatial analysis,Computer vision,Pattern recognition,Computer science,Convolutional neural network,Markov chain,Segmentation-based object categorization,Hyperspectral imaging,Image segmentation,Feature extraction,Synthetic data,Artificial intelligence | Journal |
Volume | Issue | ISSN |
27 | 5 | 1057-7149 |
Citations | PageRank | References |
17 | 0.71 | 33 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| xiangyong cao | 1 | 42 | 6.55 |
| Feng Zhou | 2 | 28 | 9.72 |
| Lin Xu | 3 | 36 | 7.52 |
| Deyu Meng | 4 | 2025 | 105.31 |
| Zongben Xu | 5 | 3203 | 198.88 |
| John Paisley | 6 | 54 | 4.63 |