Hyperspectral image classification with limited

number of labeled pixels is a challenging task. In this paper, we

propose a bilayer graph-based learning fr a mework 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.-Hyperspectral image classification with limited

number of labeled pixels is a challenging task. In this paper, we

propose a bilayer graph-based learning fr a mework 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.