Semisupervised Classification for Hyperspectral Imagery With Transductive Multiple-Kernel Learning

The classification of hyperspectral imagery is a challenging problem because few labeled pixels are available. In this letter, we propose a new semisupervised learning algorithm to combine both cluster and manifold assumptions to increase classification reliability and accuracy. The new method uses a concave-convex procedure and sequential minimization optimization technologies for transductive multiple-kernel learning (TMKL). Then, a one-against-all strategy is adopted to generalize the binary TMKL classifiers to solve the multiclass problem of remote sensing images. Experimental results on two real data sets indicate that the proposed method exhibits both high accuracy and good computational performance.