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In this study, we present spatial feature profiles that can be used in addition to spectral profiles for semisupervised small-sample-size (SSS) classification of hyperspectral images (HSIs). These profiles have been obtained by combining extended multi-attributive profiles (EMAPs) and the recently proposed Chebyshev moment multifractal profiles (CMMPs). In order to demonstrate SSS classification capabilities of the introduced feature profiles, several experiments were performed on two test HSIs. In these experiments, we used a graph-based ensemble learning method for semisupervised HSI classification and a small number of labeled samples for training. The experiments performed on test HSIs demonstrate that the proposed feature profiles provide good classification performance in terms of the overall accuracy (OA), average accuracy (AA) and Kappa statistics. We also compared the classification results obtained using EMAPs and CMMPs with those obtained using EMAPs alone, CMMPs alone, and another spatial feature sets. It has been established that the classification based on CMMPs and EMAPs shows obvious improvements, especially when the number of labeled training samples is extremely small. In the final part of the study, the HSI classification results obtained using the proposed feature profiles were compared with classification maps obtained by deep learning methods adopted for small training samples. This comparison showed that the semisupervised classification by EMAPs and CMMPs is characterized by higher values of OA, AA and Kappa coefficients. Moreover, in contrast to deep learning methods, the classification procedure based on the calculation of the proposed feature profiles and graph-based ensemble learning is not time-consuming.
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