Unsupervised hyperspectral image classification algorithm by integrating spatial-spectral information

An integrated spatial-spectral information algorithm for hyper spectral image classification is proposed, which uses spatial pixel association (SPA)by exploiting spectral information divergence (SID), and spectral clustering to reduce regions number and improve classification accuracy. Moreover, a class boundary correction method is also developed to minimize the misclassified pixels at the edge of each class and to solve the problem of merged classes. Experiments with hyper spectral data demonstrate the effectiveness and advantages of the proposed frame work over some traditional methods in term of classification accuracy.