Abstract
| Language | English |
|---|---|
| Pages | 149-154 |
| Number of pages | 6 |
| Journal | IEEE Signal Processing Magazine |
| Volume | 31 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Jul 2014 |
Fingerprint
Keywords
- hyperspectral Imaging
- feature extraction
- data reduction
- remote sensing
- covariance matrices
- hypercubes
- memory management
- principal component analysis
Cite this
}
Effective feature extraction and data reduction with hyperspectral imaging in remote sensing. / Ren, Jinchang; Zabalza, Jaime; Marshall, Stephen; Zheng, Jiangbin.
In: IEEE Signal Processing Magazine, Vol. 31, No. 4, 07.2014, p. 149-154.Research output: Contribution to journal › Special issue
TY - JOUR
T1 - Effective feature extraction and data reduction with hyperspectral imaging in remote sensing
AU - Ren, Jinchang
AU - Zabalza, Jaime
AU - Marshall, Stephen
AU - Zheng, Jiangbin
PY - 2014/7
Y1 - 2014/7
N2 - Although PCA has been widely used for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as HSI. This column analysed two variations of PCA, namely SPCA and Seg-PCA. Seg-PCA can further improve classification accuracy whilst significantly reducing the computational cost and memory requirement, without requiring prior knowledge. There is potential to apply similar feature extraction and data reduction techniques in application areas beyond HSI when analysis of large dimensional datasets is required such as magnetic resonance imaging (MRI) and digital video processing.
AB - Although PCA has been widely used for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as HSI. This column analysed two variations of PCA, namely SPCA and Seg-PCA. Seg-PCA can further improve classification accuracy whilst significantly reducing the computational cost and memory requirement, without requiring prior knowledge. There is potential to apply similar feature extraction and data reduction techniques in application areas beyond HSI when analysis of large dimensional datasets is required such as magnetic resonance imaging (MRI) and digital video processing.
KW - hyperspectral Imaging
KW - feature extraction
KW - data reduction
KW - remote sensing
KW - covariance matrices
KW - hypercubes
KW - memory management
KW - principal component analysis
UR - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=79
U2 - 10.1109/MSP.2014.2312071
DO - 10.1109/MSP.2014.2312071
M3 - Special issue
VL - 31
SP - 149
EP - 154
JO - IEEE Signal Processing Magazine
T2 - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
SN - 1053-5888
IS - 4
ER -