Maryam Imani - Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran, Iran
Hassan Ghassemian - Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran, Iran

Feature reduction is a key step in hyperspectral image classification. In this paper, we propose a supervised feature extraction method which is based on manifold learningtheory. The proposed method uses a new weighting approach in object function to makes between-class samples farther away and makes within-class samples closer in low dimensional feature space. Therefore, discriminative ability of proposed method is improved. The hyperspectral image used in our experiments is collected by AVIRIS sensor over the Indian Pines over a mixedagricultural/forest area. The experimental results show the superiority of proposed method compared to some popular and state-of-the-art feature extraction methods with using limited number of training samples

manifold learning; discriminative ability; feature extraction; hyperspectral data