  This paper proposes a novel two-stage method for the classification of
hyperspectral images. Pixel-wise classifiers, such as the classical support
vector machine (SVM), consider spectral information only; therefore they would
generate noisy classification results as spatial information is not utilized.
Many existing methods, such as morphological profiles, superpixel segmentation,
and composite kernels, exploit the spatial information too. In this paper, we
propose a two-stage approach to incorporate the spatial information. In the
first stage, an SVM is used to estimate the class probability for each pixel.
The resulting probability map for each class will be noisy. In the second
stage, a variational denoising method is used to restore these noisy
probability maps to get a good classification map. Our proposed method
effectively utilizes both spectral and spatial information of the hyperspectral
data sets. Experimental results on three widely used real hyperspectral data
sets indicate that our method is very competitive when compared with current
state-of-the-art methods, especially when the inter-class spectra are similar
or the percentage of the training pixels is high.
