Semi-supervised classification of urban hyperspectral data using spectral unmixing concepts
Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, possible connections between semi-supervised classification and spectral unmixing concepts have been rarely investigated. In this work, we propose a new method to perform semi-supervised classification of urban hyperspectral images by exploiting the information retrieved with spectral unmixing. The proposed approach integrates a well-established discriminative classifier (multinomial logistic regression) with two different spectral unmixing chains, thus bridging the gap between unmixing and classification. Moreover, the proposed method uses active learning when generating new unlabeled samples for classification.