Nonlinear mapping methods with adjustable computational complexity for hyperspectral image analysis

E. V. Myasnikov

doi:10.1117/12.2228831

8 December 2015 Nonlinear mapping methods with adjustable computational complexity for hyperspectral image analysis

E. V. Myasnikov

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 987508 (2015) https://doi.org/10.1117/12.2228831
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

Nonlinear mapping (Sammon mapping) is a well-known dimensionality reduction technique. Recently several nonlinear mapping methods with reduced computational complexity have been proposed but they do not provide a flexible control over a computational complexity. In this paper a nonlinear mapping method with adjustable computational complexity is proposed. The proposed method is based on the hierarchical decomposition of the multidimensional space, priority queues, and simple optimization procedure to provide fast and flexible dimensionality reduction process. The proposed method is compared to an alternative one based on stochastic optimization. The experiments are carried out on well-known hyperspectral images. Studied methods are evaluated in terms of the data mapping error and runtime. Experimental results for both two- and three-dimensional output spaces are presented.

Citation Download Citation

E. V. Myasnikov "Nonlinear mapping methods with adjustable computational complexity for hyperspectral image analysis", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987508 (8 December 2015); https://doi.org/10.1117/12.2228831

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