Feature extraction framework in class space for hyperspectral image classification

In this paper, a novel feature extraction framework is proposed for hyperspectral image classification. Inspired by the role of discriminant function in classifier, which intends to learn a mapping from the input features to label information in class space, we develop a feature extraction framework to learn the new feature representation of original input features in class space, by establishing the relevance between feature extraction and discriminative classifier. The new learning features integrate the input features and the discrimination information of used classifier with available training samples, which reveal the cues of class in class space. Therefore, the new features are called as the features of class-in-class. Several experiments were conducted to illustrate the availability of the proposed features.