Manifold learning based supervised hyperspectral data classification method using class encoding

Miao Zhang, Wei Guo, Yiming Cui, Fei Shen, Yi Shen

Abstract: Manifold learning based unsupervised classification methods will be unable to obtain satisfactory results because of the lack of training samples. The employment of training samples' information makes manifold learning based classification become supervised, and thus brings the improvement on classification accuracy. In order to make full use of this information, we emphatically consider the hyperspectral data distribute by clusters. A novel supervised manifold learning method termed class encoding is proposed for hyperspectral data classification. The experimental results show that this algorithm has better classification performance than the existing supervised manifold learning algorithm.