Affiliations: [1] Group of Supercomputation-Algorithms, Department of Informatics, University of Almería, ceiA3, 04120, Almería, Spain | [2] Computer Architecture Department, Campus Teatinos, Universidad de Málaga, 29010, Málaga, Spain | [3] Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08663, Vilnius, Lithuania. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Correspondence:
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Corresponding author.
Abstract: The isometric mapping (Isomap) algorithm is often used for analysing hyperspectral images. Isomap allows to reduce such hyperspectral images from a high-dimensional space into a lower-dimensional space, keeping the critical original information. To achieve such objective, Isomap uses the state-of-the-art MultiDimensional Scaling method (MDS) for dimensionality reduction. In this work, we propose to use Isomap with SMACOF, since SMACOF is the most accurate MDS method. A deep comparison, in terms of accuracy, between Isomap based on an eigen-decomposition process and Isomap based on SMACOF has been carried out using three benchmark hyperspectral images. Moreover, for the hyperspectral image classification, three classifiers (support vector machine, k-nearest neighbour, and Random Forest) have been used to compare both Isomap approaches. The experimental investigation has shown that better classification accuracy is obtained by Isomap with SMACOF.
