To avoid the disadvantage of getting into local optimum solution with general numerical computation methods in the general independent component analysis and the restriction of neuron activation functions of neural learning algorithm,an improved model of independent component analysis(ICA) based on genetic algorithm was proposed for the unsupervised classification of hyperspectral data.

In order to classify the data of Hyperspectral remote sensing images automatically without prior knowledge,an unsupervised classification algorithm is presented based on the conception of convex geometry and spectral features in this paper.

In this study,a novel artificial immune system algorithm for unsupervised classification and recognition is proposed by using a novel manifold distance based dissimilarity measure which can measure the geodesic distance along the manifold.