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

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

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    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.

    Creators(s): 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;
    Item type:Article
    ID code:48750
    Keywords:hyperspectral Imaging, feature extraction, data reduction, remote sensing, covariance matrices, hypercubes, memory management, principal component analysis, Electrical engineering. Electronics Nuclear engineering, Electronic computers. Computer science, Signal Processing, Applied Mathematics, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Technology and Innovation Centre > Sensors and Asset Management
    Depositing user: Pure Administrator
    Date deposited:24 Jun 2014 11:09
    Last modified:20 Jan 2021 21:18
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/48750
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