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This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial...
In this work, we present a new subspace discriminant analysis classification algorithmfor remotely sensed hyperspectral image data. Our motivation for including subspace projection as a distinctive feature of our work is to better model noise and mixed pixels present in hyperspectral images. Two different dimensionality reduction techniques are considered: principal component analysis (PCA) and the...
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