Introduction - If you have any usage issues, please Google them yourself
Hyperspectral image classification with limited
number of labeled pixels is a challenging task. In this paper, we
propose a bilayer graph-based learning framework to address
this problem. For graph-based classification, how to establish
the neighboring relationship among the pixels the high
dimensional features is the key toward a successful classification.
Our graph learning algorithm contains two layers. The first-layer
constructs a simple graph, where each vertex denotes one pixel
and the edge weight encodes the similarity between two pixels.
Unsupervised learning is then conducted to estimate the grouping
relations among different pixels. These relations are subsequently
fed into the second layer to form a hypergraph structure, on top
of which, semisupervised transductive learning is conducted to
obtain the final classification results. Our experiments on three
data sets demonstrate the merits of our proposed approach,
which compares favorably with state of the art.