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20 May 2011 Trilateral filter on multispectral imagery for classification and segmentation
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In this paper, we present a new approach to filtering high spatial resolution multispectral (MSI) or hyperspectral imagery (HSI) for the purpose of classification and segmentation. Our approach is inspired by the bilateral filtering method that smooths images while preserving important edges for gray-scale and color images. To achieve a similar goal for MSI/HSI, we build a nonlinear tri-lateral filter that takes into account both spatial and spectral similarities. Our approach works on a pixel by pixel basis; the spectrum of each pixel in the filtered image is the combination of the spectra of its adjacent pixels in the original image weighted by the three factors: geometric closeness, spectral Euclidean distance and spectral angle separation. The approach reduces small clutter across the image while keeping edges with strong contrast. The improvement of our method is that we use both spectral intensity differences together with spectral angle separation as the closeness metric, thus preserving edges caused both by material as well as by similar materials with intensity differences. A k-means classifier is applied to the filtered image and the results show our approach can produce a much less cluttered class map. Results will be shown using imagery from the Digital Globe Worldview-2 multispectral sensor and the HYDICE hyperspectral sensor. This approach could also be expanded to facilitate feature extraction from MSI/HSI.
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Weihua Sun and David W. Messinger "Trilateral filter on multispectral imagery for classification and segmentation", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80480Y (20 May 2011);

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