  In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction
and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify
hyperspectral images, which makes full use of spatial and spectral information.
The Shape-adaptive Reconstruction (SaR) is introduced to preprocess each pixel
based on the Pearson Correlation between pixels in its shape-adaptive (SA)
region. Support Vector Machines (SVMs) are trained to estimate the pixel-wise
probability maps of each class. Then the Smoothed Total Variation (STV) model
is applied to denoise and generate the final classification map. Experiments
show that SaR-SVM-STV outperforms the SVM-STV method with a few training
labels, demonstrating the significance of reconstructing hyperspectral images
before classification.
