Effective feature extraction and data reduction with hyperspectral imaging in remote sensing

Tools

Ren, Jinchang and Zabalza, Jaime and Marshall, Stephen and Zheng, Jiangbin (2014) Effective feature extraction and data reduction with hyperspectral imaging in remote sensing. IEEE Signal Processing Magazine, 31 (4). pp. 149-154. ISSN 1053-5888 (https://doi.org/10.1109/MSP.2014.2312071)

[thumbnail of jren-SPM-2014]
Preview
 PDF. Filename: jren_SPM_2014.pdf
Preprint
Download (877kB)| Preview

Abstract

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.

ORCID iDs

Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725, Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628 and Zheng, Jiangbin;
CORE (COnnecting REpositories)