[IPOL announce] new article: Hyperspectral Image Classification Using Graph Clustering Methods

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2017/204/

Zhaoyi Meng, Ekaterina Merkurjev, Alice Koniges, and Andrea L. Bertozzi,
Hyperspectral Image Classification Using Graph Clustering Methods,
Image Processing On Line, 7 (2017), pp. 218–245.
https://doi.org/10.5201/ipol.2017.204

Abstract
Hyperspectral imagery is a challenging modality due to the dimension of 
the pixels which can range from hundreds to over a thousand frequencies 
depending on the sensor. Most methods in the literature reduce the 
dimension of the data using a method such as principal component 
analysis, however this procedure can lose information. More recently, 
methods have been developed to address classification of large datasets 
in high dimensions. This paper presents two classes of graph-based 
classification methods for hyperspectral imagery. Using the full 
dimensionality of the data, we consider a similarity graph based on 
pairwise comparisons of pixels. The graph is segmented using a 
pseudospectral algorithm for graph clustering that requires information 
about the eigenfunctions of the graph Laplacian but does not require 
computation of the full graph. We develop a parallel version of the 
Nyström extension method to randomly sample the graph to construct a low 
rank approximation of the graph Laplacian. With at most a few hundred 
eigenfunctions, we can implement the clustering method designed to solve 
a variational problem for a graph-cut-based semi-supervised or 
unsupervised classification problem. We implement OpenMP directive-based 
parallelism in our algorithms and show performance improvement and 
strong, almost ideal, scaling behavior. The method can handle very large 
datasets including a video sequence with over a million pixels, and the 
problem of segmenting a data set into a pre-determined number of classes.