  The inclusion of spatial information into spectral classifiers for
fine-resolution hyperspectral imagery has led to significant improvements in
terms of classification performance. The task of spectral-spatial hyperspectral
image classification has remained challenging because of high intraclass
spectrum variability and low interclass spectral variability. This fact has
made the extraction of spatial information highly active. In this work, a novel
hyperspectral image classification framework using the fusion of dual spatial
information is proposed, in which the dual spatial information is built by both
exploiting pre-processing feature extraction and post-processing spatial
optimization. In the feature extraction stage, an adaptive texture smoothing
method is proposed to construct the structural profile (SP), which makes it
possible to precisely extract discriminative features from hyperspectral
images. The SP extraction method is used here for the first time in the remote
sensing community. Then, the extracted SP is fed into a spectral classifier. In
the spatial optimization stage, a pixel-level classifier is used to obtain the
class probability followed by an extended random walker-based spatial
optimization technique. Finally, a decision fusion rule is utilized to fuse the
class probabilities obtained by the two different stages. Experiments performed
on three data sets from different scenes illustrate that the proposed method
can outperform other state-of-the-art classification techniques. In addition,
the proposed feature extraction method, i.e., SP, can effectively improve the
discrimination between different land covers.
