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Sequential spectral clustering of hyperspectral remote sensing image over bipartite graph
- Source :
- Applied Soft Computing. 73:727-734
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- 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.
- Subjects :
- Computer science
0211 other engineering and technologies
Hyperspectral imaging
02 engineering and technology
Spectral clustering
Image (mathematics)
ComputingMethodologies_PATTERNRECOGNITION
Singular value decomposition
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
Bipartite graph
020201 artificial intelligence & image processing
Representation (mathematics)
Cluster analysis
Software
021101 geological & geomatics engineering
Remote sensing
Subjects
Details
- ISSN :
- 15684946
- Volume :
- 73
- Database :
- OpenAIRE
- Journal :
- Applied Soft Computing
- Accession number :
- edsair.doi...........2aca15040b050b751cdff37b1d84f8b9