Title :
Robust Linear Spectral Unmixing Using Anomaly Detection
Author :
Altmann, Yoann ; McLaughlin, Steve ; Hero, Alfred
Author_Institution :
Sch. of Eng. & Phys. Sci., Heriot-Watt Univ., Edinburgh, UK
fDate :
6/1/2015 12:00:00 AM
Abstract :
This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modeling anomalies, and additive Gaussian noise. A Markov random field is used for anomaly detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and anomaly detection algorithm. Simulations conducted with synthetic and real hyperspectral images demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.
Keywords :
Markov processes; hyperspectral imaging; image classification; white noise; Bayesian algorithm; Markov random field; additive Gaussian noise; anomaly detection algorithm; hyperspectral images; linear spectral unmixing; nonlinear term modeling anomalies; outlier detection; Bayes methods; Computational modeling; Estimation; Joints; Licenses; Noise; Robustness; Bayesian estimation; Hyperspectral imagery; MCMC; anomaly detection; unsupervised spectral unmixing;
Journal_Title :
Computational Imaging, IEEE Transactions on
DOI :
10.1109/TCI.2015.2455411