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13 May 2010 Spherical harmonics as a shape descriptor for hyperspectral image classification
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Hyperspectral images have traditionally been analyzed by pixel based methods. Invariant methods that consider surface and shape geometry have not been used with these images. However, there is a need for such methods due to the spectral and spatial variability present in these images. In this paper, we develop a method for classifying these images invariant to translation and rotation. The method is based on developing shape descriptors using spherical harmonics. These orthogonal functions have been widely used as a powerful tool for 3D shape recognition and are better suited for hyperspectral images due to its inherent dimensionality. A spherical function defined on the surface of a shape extracts rotation invariant features. In this case, the hyperspectral image is converted to spherical coordinates, decomposed as a sum of its harmonics and then converted to Cartesian coordinates. A classifier is trained with spherical harmonic descriptors computed from training samples. Support vector machines and Maximum Likelihood are considered for classification. The method is tested with hyperspectral image from AISA, AVIRIS and HYDICE sensors. The results show that the descriptors are effective in improving the accuracy of classification.
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Fanny Nina-Paravecino and Vidya Manian "Spherical harmonics as a shape descriptor for hyperspectral image classification", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951E (13 May 2010);

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