ABSTRACT
Spectral imaging typically generates a large amount of high-dimensional data that are acquired in different sub-bands for each spatial location of interest. The high dimensionality of spectral data imposes limitations on numerical analysis. As such, there is an emerging demand for robust data compression techniques with loss of less relevant information to manage real spectral data. In this paper, we describe a reduced-order data modeling technique based on local proper orthogonal decomposition (POD) in order to compute low-dimensional models by projecting high-dimensional clusters onto subspaces spanned by local reduced-order bases. We refer to the proposed method as the local-based approach because POD finds locally optimal solutions on each group split by k-means clustering. Experimental results are reported on three public domain databases and an in-house database. Comparisons with three leading spectral recovery techniques, three decomposition techniques used for hyperspectral imaging, and two baseline techniques show that the proposed method leads to promising improvement on spectral and colorimetric accuracy corresponding to the reconstructed spectral reflectance.
