Using k-MST, k-EC, and k-VC neighbor graphs construction methods with spatial coherent distance for manifold learning in hyperspectral image processing

Nicolas Rey-Villamizar; Miguel Vélez-Reyes

doi:10.1117/12.851725

12 May 2010 Using k-MST, k-EC, and k-VC neighbor graphs construction methods with spatial coherent distance for manifold learning in hyperspectral image processing

Nicolas Rey-Villamizar, Miguel Vélez-Reyes

Author Affiliations +

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76950E (2010) https://doi.org/10.1117/12.851725
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Dimensionality reduction is a key step in hyperspectral image processing. Recent investigations are looking into nonlinear manifold learning for dimensionality reduction in hyperspectral imagery. Nonlinear manifold learning methods such as Isomap, and Local Linear Embedding are use to recover the low dimensional representation of an unknown nonlinear manifold for high dimensional data where it is important to retain the neighborhood structure of the manifold. Although these algorithms use different philosophies for recovering the nonlinear manifold, they all incorporate neighborhood information from each data point to construct a weighted graph having the data points as vertices. Thus the performance of these methods highly depends on how well these neighborhoods are selected, since all subsequent steps rely on it. The k-NN algorithm is the most widely used technique for neighbor selection in manifold learning. However, it can result in a disconnected graph and it does not fully exploit spatial neighborhood information from the image in selecting points to form the neighborhoods. In this paper, recently proposed methods for constructing the weighted graph in manifold learning are studied: k-VC, k-EC and k-MST, which have the advantage of creating connected graphs and have performed well in artificial data sets. Spatial information of the hyperspectral images is included in the manifold learning process by using spatial coherence. Experiments are conducted with artificial data and hyperspectral images. For hyperspectral images, classification accuracy was used as an indirect measure to understand how the low dimensional embedding of the data reduces dimensionality while still maintaining good discrimination performance.

Citation Download Citation

Nicolas Rey-Villamizar and Miguel Vélez-Reyes "Using k-MST, k-EC, and k-VC neighbor graphs construction methods with spatial coherent distance for manifold learning in hyperspectral image processing", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76950E (12 May 2010); https://doi.org/10.1117/12.851725

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