Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Independent Component Analysis (ICA). This method, instead of transforming the original hyperspectral images, evaluates the weight matrix to observe how each band contributes to the ICA unmixing procedure. It compares the average absolute weight coefficients of individual spectral bands and selects bands that contain more information. As a significant benefit, the ICA-based band selection retains most physical features of the spectral profiles given only the observations of hyperspectral images. We compare this method with ICA transformation and Principal Component Analysis (PCA) transformation on classification accuracy. The experimental results show that ICA-based band selection is more effective in dimensionality reduction for HSI analysis.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Band selection using independent component analysis for hyperspectral image processing


    Beteiligte:
    Hongtao Du, (Autor:in) / Hairong Qi, (Autor:in) / Xiaoling Wang, (Autor:in) / Ramanath, R. (Autor:in) / Snyder, W.E. (Autor:in)


    Erscheinungsdatum :

    2003-01-01


    Format / Umfang :

    365548 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Band Selection Using Independent Component Analysis for Hyperspectral Image Processing

    Du, H. / Qi, H. / Wang, X. et al. | British Library Conference Proceedings | 2004



    Using Feature-Vector Based Analysis, Based on Principal Component Analysis and Independent Component Analysis, for Analyzing Hyperspectral Images

    Muhammed, H. / Ammenberg, P. / Bengtsson, E. et al. | British Library Conference Proceedings | 2001


    Content Based Multispectral Image Retrieval Using Independent Component Analysis

    Shahbazi, Hamed / Kabiri, Peyman / Soryani, Mohsen | IEEE | 2008