Name
Title | ||
|---|---|---|
A New Spatial-Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation. |
Abstract | ||
|---|---|---|
In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial-contextual information in the scene when conducting feature extraction (FE) from hyperspectral images (HSIs). Specifically, our method first projects the HSI into a subspace, using the maximum noise fraction method. Then, for eac... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TGRS.2018.2801387 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Feature extraction,Correlation,Covariance matrices,Iron,Hyperspectral imaging,Principal component analysis,Support vector machines | Computer vision,Subspace topology,Pattern recognition,Support vector machine,Feature extraction,Hyperspectral imaging,Artificial intelligence,Pixel,Covariance matrix,Spectral bands,Mathematics,Principal component analysis | Journal |
Volume | Issue | ISSN |
56 | 6 | 0196-2892 |
Citations | PageRank | References |
7 | 0.47 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leyuan Fang | 1 | 116 | 11.15 |
| Nanjun He | 2 | 42 | 3.70 |
| Shutao Li | 3 | 191 | 16.15 |
| Antonio Plaza | 4 | 83 | 17.35 |
| Javier Plaza | 5 | 298 | 30.10 |