Semi-blind Unmixing of Hyperspectral Image by Eigenvalue based Virtual Dimensionality Estimation

This paper proposes a new approach to perform unmixing of hyperspectral image with the help of spectral library. The work introduces the concept of virtual dimensionality to unmixing

Semi-blind Unmixing of Hyperspectral Image by Eigenvalue based Virtual Dimensionality Estimation

This paper proposes a new approach to perform unmixing of hyperspectral image with the help of spectral library. The work introduces the concept of virtual dimensionality to unmixing

VIRTUAL DIMENSIONALITY ESTIMATION IN HYPERSPECTRAL IMAGERY BASED ON UNSUPERVISED FEATURE SELECTION

Virtual Dimensionality (VD) is a concept developed to estimate the number of distinct spectral signatures in hyperspectral imagery. Intuitively, detecting the number of spectrally distinct signatures depends on determining the number of distinct bands of the data. Considering this idea, the current paper aims at estimating the VD based on finding independent bands in the image partition space. Eventually, the number of independent selected bands is accepted as the VD estimate. The proposed method is automatic and distribution-free. In addition, no tuning parameters and noise estimation processes are needed. This method is compared with three well-known VD estimation methods using synthetic and real datasets. Experimental results show high speed and reliability in the performance of the proposed method.