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An Evaluation of Low-Rank Mahalanobis Metric Learning Techniques for Hyperspectral Image Classification

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1 Author(s)
Bue, B.D. ; Machine Learning & Instrum. Autonomy Group, NASA Jet Propulsion Lab., Pasadena, CA, USA

We provide a comparative study of several state-of-the-art Mahalanobis metric learning algorithms evaluated on three well-studied, high-dimensional hyperspectral images captured by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument. We focus on the problem of low-rank Mahalanobis metric learning, where our objective is to learn an n × m projection matrix A, where m ≪ n. Low-rank metrics offer a “plug-in” enhancement to similarity-based classifiers that can reduce computation time and improve classification accuracy with fewer training samples, enabling operations in resource-constrained environments such as onboard spacecraft. Our results indicate that applying a simple shrinkage-based regularization procedure to multiclass Linear Discriminant Analysis (LDA) produces comparable or better classification accuracies than the low-rank extensions of several widely used Mahalanobis metric learning algorithms, at considerably lower computational cost.

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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:7 ,  Issue: 4 )