Sample-screening MKL method via boosting strategy for hyperspectral image classification

The problem of limited training samples is always a major concern in hyperspectral remote sensing image classification. In this paper, a sample-screening multiple kernel learning (S2MKL) method is proposed for hyperspectral image classification with limited training samples. The core idea of the proposed method is to employ boosting strategy for screening the limited training samples under MKL framework. Different from existing methods, the proposed MKL method exploits the boosting trick to try different combinations of the limited training samples and adaptively determine the optimal weights of base kernels in the linear combination. Morphological profiles are firstly extracted as the both spatial and spectral features for classification instead of the original spectra. With the morphological profiles, AdaBoost strategy is then introduced to guide the construction of multiple kernel learning machine. By means of boosting strategy, the limited samples are effectively screened and used for classification. Meanwhile, the weights of base kernels in the linear combination are automatically determined in the process of screening samples. Three real hyperspectral data sets are used to evaluate the proposed method. The experimental results show that the proposed boosting-based multiple kernel learning method is superior to state-of-the-art methods in terms of classification performance while limited samples are used.