Texture analysis of IKONOS satellite imagery for urban land use and land cover classification

Traditional spectral-based methods of extracting urban land cover and land use information from remote sensing imagery have proven to be unsuitable for high spatial resolution images. Texture has been widely investigated as a supplement to spectral data for the analysis of complex urban scenes. This...

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Bibliographic Details
Published in:The imaging science journal 2010-06, Vol.58 (3), p.163-170
Main Authors: Kabir, S, He, D-C, Sanusi, M A, Wan Hussina, W M A
Format: Article
Language:English
Subjects:
CLASSIFICATION
HIGH-RESOLUTION
SATELLITE IMAGERY
TEXTURE ANALYSIS
URBAN ENVIRONMENT
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Summary:Traditional spectral-based methods of extracting urban land cover and land use information from remote sensing imagery have proven to be unsuitable for high spatial resolution images. Texture has been widely investigated as a supplement to spectral data for the analysis of complex urban scenes. This research evaluates the grey level co-occurrence matrix (GLCM) texture analysis technique and the maximum likelihood classification approach for the extraction of texture features to be combined with spectral data, as a method for obtaining more accurate urban land cover and land use information from high spatial resolution images. Classifications were performed on IKONOS imagery using three datasets: a spatial dataset consisting of three texture images (mean, homogeneity and dissimilarity), a spectral dataset consisting of four spectral images (red, green, blue and NIR) and a combination dataset (spatial and spectral). Results show that the combination dataset produced the highest overall classification accuracy of 86.1%, an improvement of 7.2% over the spectral dataset.
ISSN:1368-2199
1743-131X
DOI:10.1179/136821909X12581187860130