Graph-based denoising and classification of hyperspectral imagery using nonlocal operators

Alexey Castrodad

doi:10.1117/12.818732

27 April 2009 Graph-based denoising and classification of hyperspectral imagery using nonlocal operators

Alexey Castrodad

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 73340E (2009) https://doi.org/10.1117/12.818732
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Several studies have shown that the use of nonlocal operators can significantly remove noise and improve the quality of natural images. These operators are built on similarities between small local neighborhoods that are not necessarily spatially close, which plays a very important role in preserving the image structure, and are closely related to the kernel methods used in manifold learning and nonlinear dimension reduction. This serves as our motivation for exploring the use of nonlocal, linear, and nonlinear diffusion processes on high dimensional imagery (e.g. hyperspectral) that do not require the computation of eigenfunctions. We utilize the same iterative scheme to perform a semi-supervised multi-class classification and segmentation, only by changing the initial conditions. Furthermore, we compare the denoising performance of these algorithms with other PDE-based methods like anisotropic diffusion and compare classification accuracies for different materials on real Hyperspectral Image (HSI) cubes.

Citation Download Citation

Alexey Castrodad "Graph-based denoising and classification of hyperspectral imagery using nonlocal operators", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340E (27 April 2009); https://doi.org/10.1117/12.818732

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