MULTIVARIATE MATHEMATICAL MORPHOLOGY BASED ON PRINCIPAL COMPONENT ANALYSIS : INITIAL RESULTS IN BUILDING EXTRACTION

Today, colour or multichannel satellite and aerial images are increasingly becoming available due to the commercial availability of multispectral digital sensors and pansharpening function of the commercial remote sensing software tools. Comparing to their monochromic counterparts, colour image data can offer not only more useful information about landscape but also the correlations among channels. Recently, multivariate mathematical morphology has received increased attention due to its rigorous mathematical theory and its powerful utility in multichannel image analysis. In this paper, a new morphological method for multichannel remotely sensed image processing is presented and analyzed. The proposed method utilizes a multivariate ordering principle based on principal component analysis. To define the colour morphology the colour vectors are ordered by using the first principal component analysis. On the basis of this ordering, new infimum and supremum are defined. Using the new infimum and supremum, the fundamental erosion and dilation operations are defined. Two series of experiments have been prepared to test the performance of the proposed method by using Ikonos and QuickBird pansharpened images and colour aerial images acquired over a built-up area. * Corresponding author. Dr. Jonathan Li, P.Eng., O.L.S., Assistant Professor of Geomatics Engineering, Ryerson University

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