Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity

by   Tatsumi Uezato, et al.

Spectral variability is one of the major issue when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember classes), the spectral signature characterizing these classes may spatially vary due to intrinsic component fluctuations or external factors (illumination). These redundant multiple endmember spectra within each class adversely affect the performance of unmixing methods. This paper proposes a mixing model that explicitly incorporates a hierarchical structure of redundant multiple spectra representing each class. The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel. The resulting unmixing algorithm is able to adaptively recover several bundles of endmember spectra associated with each class and robustly estimate abundances. In addition, its flexibility allows a variable number of classes to be present within each pixel of the hyperspectral image to be unmixed. The proposed method is compared with other state-of-the-art unmixing methods that incorporate sparsity using both simulated and real hyperspectral data. The results show that the proposed method can successfully determine the variable number of classes present within each class and estimate the corresponding class abundances.


page 20

page 23


Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images

Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectr...

Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images

Hyperspectral unmixing aims at identifying a set of elementary spectra a...

Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-pixel Quantification

The estimation of land cover fractions from remote sensing images is a f...

Illumination invariant hyperspectral image unmixing based on a digital surface model

Although many spectral unmixing models have been developed to address sp...

Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation

This paper proposes a new hyperspectral unmixing method for nonlinearly ...

Target Identification and Bayesian Model Averaging with Probabilistic Hierarchical Factor Probabilities

Target detection in hyperspectral imagery is the process of locating pix...

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on su...

Please sign up or login with your details

Forgot password? Click here to reset