Performance evaluation for data reduction techniques of hyperspectral images

A.K. Singh, J.K. Kishore, G.R. Kadambi, James Shuttleworth

    Research output: Contribution to conferencePaper

    1 Citation (Scopus)


    Hyperspectral image contains information as a set of contiguous spectral wavelength bands. These images are three dimensional images having up to two hundred bands or more. Although hyperspectral image contain hundreds of bands, a few number of bands can explain the vast majority of the information. Hence, hyperspectral image is transformed into lower dimension by preserving the main features of the original data using dimension reduction techniques, which eliminate data redundancy. In order to reduce the complexity and the time taken for processing of the hyperspectral images, Principal Component Analysis (PCA) is used to reduce the dimensionality while retaining all important features of the image. In this paper, PCA and Noise Adjusted Principal Component Analysis (NAPCA) have been simulated in the area of hyperspectral image processing. Experiments were carried out on three different types of aerial hyperspectral images having different spectral and spatial resolutions. An elaborate performance evaluation for these data reduction techniques has been carried out by taking image quality parameters such as Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). Image quality parameters are calculated using original hyperspectral image and reconstructed images of PCA and NAPCA.
    Original languageEnglish
    Pages64 - 68
    Publication statusPublished - 2014
    EventInternational Conference on Circuits, Communication, Control and Computing - Bangalore, India
    Duration: 21 Nov 201422 Nov 2014


    ConferenceInternational Conference on Circuits, Communication, Control and Computing
    Abbreviated titleI4C 2014

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    • aerial photography
    • data reduction
    • image analysis
    • image processing
    • image quality
    • independent component analysis
    • mean square error
    • quality control
    • signal to noise ratio
    • spectroscopy
    • dimension reduction techniques
    • eliminate data redundancies
    • hyper-spectral images
    • hyperspectral image processing
    • noise adjusted PCA
    • noise adjusted principal component analysis
    • peak signal to noise ratio
    • three dimensional images
    • principal component analysis


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