Name
Abstract | ||
|---|---|---|
Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1186/1687-6180-2011-66 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
Hyper-spectral data, feature extraction, fuzzy sets, material classification | Data mining,Decorrelation,Computer science,Artificial intelligence,Discriminative model,Computer vision,Spectral imaging,Pattern recognition,Feature extraction,Curse of dimensionality,Statistical model,Linear discriminant analysis,Principal component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
2011 | 1 | 1687-6180 |
Citations | PageRank | References |
7 | 0.41 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Artzai Picón | 1 | 121 | 10.60 |
| Ovidiu Ghita | 2 | 234 | 18.12 |
| Sergio Rodríguez-Vaamonde | 3 | 25 | 2.73 |
| Pedro M. Iriondo | 4 | 36 | 4.69 |
| Paul F. Whelan | 5 | 561 | 39.95 |