Abstract
Hyperspectral imaging (HSI) devices produce 3-D hyper-cubes of a spatial scene in hundreds of different spectral bands, generating large data sets which allow accurate data processing to be implemented. However, the large dimen-sionality of hypercubes leads to subsequent implementation of dimensionality reduction techniques such as principal component analysis (PCA), where the covariance matrix is constructed in order to perform such analysis. In this paper, we describe how the covariance matrix of an HSI hyper-cube can be computed in real time ‘on the fly’ during the data acquisition process. This offers great potential for HSI embedded devices to provide not only conventional HSI data but also preprocessed information.
Original language | English |
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Pages | 749 - 753 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 1 Sep 2015 |
Event | 23rd European Signal Processing Conference, 2015 (EUSIPCO 2015) - Nice, France Duration: 31 Aug 2015 → 4 Sep 2015 |
Conference
Conference | 23rd European Signal Processing Conference, 2015 (EUSIPCO 2015) |
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Abbreviated title | EUSIPCO 2015 |
Country/Territory | France |
City | Nice |
Period | 31/08/15 → 4/09/15 |
Keywords
- covariance matrix
- principal component analysis (PCA)
- hyperspectral cameras
- hypercube
- data reduction