Two New Indices for Unsupervised Dimensionality Reduction of Hyperspectral Data

Hyperspectral imaging, with hundreds of spectral bands provide a rich data source for thematic mapping. However, the high dimensionality of these images is a challenge which is commonly treated by dimensionality reduction techniques. In this paper, a pixel-based hyperspectral dimensionality reduction technique is proposed in which the spectral signature curve (SSC) is divided into segments with equal lengths. Afterwards two new mathematical indices, which are based on integral and L2-norm, are proposed to transform each SSC segment into a new feature of the reduced feature vector. The proposed method considers the ordinance of SSC and, unlike PCA, can be applied individually to all pixels independently and simultaneously which means the method is applicable in parallel processing. The proposed data reduction technique was applied to two well-known agricultural hyperspectral scenes and was compared to wavelet and PCA data reduction techniques. The obtained results proved the efficiency of the proposed method from both classification accuracy and processing time aspects.