A1 Journal article (refereed), original research

Sequential spectral clustering of hyperspectral remote sensing image over bipartite graph

Publication Details
Authors: Hassanzadeh Aidin, Kaarna Antti, Kauranne Tuomo
Publisher: Elsevier
Publication year: 2018
Language: English
Related Journal or Series Information: Applied Soft Computing
Volume number: 73
Start page: 727
End page: 734
Number of pages: 8
ISSN: 1568-4946
eISSN: 1872-9681
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication


Unsupervised classification
is a crucial step in remote sensing hyperspectral image analysis where
producing labeled data is a laborious task. Spectral Clustering
is an appealing graph-partitioning technique with outstanding
performance on data with non-linear dependencies. However, Spectral
Clustering is restricted to small-scale
data and neither has been effectively applied to hyperspectral image
analysis. In this paper, the unsupervised classification of
hyperspectral images is addressed through a sequential spectral
clustering that can be extended to the large-scale hyperspectral image.
To this end, this paper utilizes a bipartite graph representation along with a sequential singular value decomposition and mini-batch K-means for unsupervised classification of hyperspectral imagery. We evaluate the proposed algorithm with several benchmark hyperspectral datasets including Botswana, Salinas, Indian Pines, Pavia Center Scene and Pavia University Scene. The experimental results show significant improvements made by the proposed algorithm compared to the state-of-art clustering algorithms.

Last updated on 2019-13-03 at 12:00