  Hyperspectral images often have hundreds of spectral bands of different
wavelengths captured by aircraft or satellites that record land coverage.
Identifying detailed classes of pixels becomes feasible due to the enhancement
in spectral and spatial resolution of hyperspectral images. In this work, we
propose a novel framework that utilizes both spatial and spectral information
for classifying pixels in hyperspectral images. The method consists of three
stages. In the first stage, the pre-processing stage, Nested Sliding Window
algorithm is used to reconstruct the original data by {enhancing the
consistency of neighboring pixels} and then Principal Component Analysis is
used to reduce the dimension of data. In the second stage, Support Vector
Machines are trained to estimate the pixel-wise probability map of each class
using the spectral information from the images. Finally, a smoothed total
variation model is applied to smooth the class probability vectors by {ensuring
spatial connectivity} in the images. We demonstrate the superiority of our
method against three state-of-the-art algorithms on six benchmark hyperspectral
data sets with 10 to 50 training labels for each class. The results show that
our method gives the overall best performance in accuracy. Especially, our gain
in accuracy increases when the number of labeled pixels decreases and therefore
our method is more advantageous to be applied to problems with small training
set. Hence it is of great practical significance since expert annotations are
often expensive and difficult to collect.
