Sparse Spatio-Spectral LapSVM With Semisupervised Kernel Propagation for Hyperspectral Image Classification

The information contained in the hyperspectral data allows the characterization, identification, and classification of the land covers with improved accuracy and robustness. Many methods have been explored in the hyperspectral image classification (HIC). Among these methods, spatio-spectral Laplacian support vector machine (SS-LapSVM) combines the spatial and spectral information on both the labeled and unlabeled samples together through the weight sum of a spectral regularization term and a spatial regularization term. Thus, it can achieve accurate classification with very few labeled samples and has proved to be effective in HIC. In this paper, a sparse SS-LapSVM with semisupervised Kernel Propagation (S3LapSVM-KP) is constructed to achieve higher accuracy and efficiency in HIC. First, data-driven semisupervised KP is proposed to carefully learn a kernel matrix from a small number of labeled pixels. Furthermore, a one-step sparse pruning algorithm is advanced by solving sparse weight vectors associated with network nodes in SS-LapSVM. By combining semisupervised KP with sparse coding, S3LapSVM-KP can not only automatically determine kernels from data, but also avoid overfitting and reduce computation cost resulted from the nonsparse topology of SS-LapSVM. The performance of S3LapSVM-KP is evaluated on several real hyperspectral datasets, and the results show that S3LapSVM-KP can achieve accurate and rapid classification with very few labeled data.