A Comparative Study of Unsupervised Unmixing Algorithms to Detecting Anomalies in Hyperspectral Images

Abstract

In this paper, we present a comparative study of several unsupervised unmixing algorithms to anomaly detection in hyperspectral images. The algorithms are called minimum volume constrained non-negative matrix factorization (MVCNMF) [1], gradient descent maximum entropy (GDME) [2] and unsupervised fully constrained least squares (UFCLS) [3] . Several… (More)

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