Feature Identification via a Combined ICA-Wavelet Method for Image Information Mining

Feature Identification via a Combined ICA-Wavelet Method for Image Information Mining

Image transformation is required for color-texture image segmentation. Various techniques are available for the transformation along the spatial and spectral axes. For instance, the HSV-wavelet technique is shown to be very effective for image information mining in remote-sensing applications. Howev...

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Veröffentlicht in:IEEE geoscience and remote sensing letters 2010-01, Vol.7 (1), p.18-22
Hauptverfasser: Shah, V.P., Younan, N.H., Durbha, S.S., King, R.L.
Format: Artikel
Sprache:eng
Schlagworte:
Archives
Data mining
Earth
Feature extraction
Feature fusion
Image databases
image information mining
Image retrieval
Image segmentation
Independent component analysis
independent component analysis (ICA)
Information retrieval
Mining
Spatial databases
Spectra
Spectral bands
Transformations
Wavelet analysis
wavelets
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Beschreibung
Zusammenfassung:Image transformation is required for color-texture image segmentation. Various techniques are available for the transformation along the spatial and spectral axes. For instance, the HSV-wavelet technique is shown to be very effective for image information mining in remote-sensing applications. However, the HSV transformation approach uses only three spectral bands at a time. In this letter, a new feature set, obtained by combining independent component analysis and wavelet transformation for image information mining in geospatial data, is presented. Experimental results show the effectiveness of the presented method for image information mining in Earth observation data archives.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2009.2020519