Clustered multitask non-negative matrix factorization for spectral unmixing of hyperspectral data
Copyright policy )Clustered multitask non-negative matrix factorization for spectral unmixing of hyperspectral data
In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed. Each pixel in the hyperspectral image considered as a node in this network. The nodes in the network are clustered using the fuzzy c-means clustering method. Diffusion least mean square strategy has been used to optimize the proposed cost function. To evaluate the proposed method, experiments are conducted on synthetic and real datasets. Simulation results based on spectral angle distance, abundance angle distance and reconstruction error metrics illustrate the advantage of the proposed algorithm compared with other methods.
one column, 22 pages, 12 figures, journal. arXiv admin note: substantial text overlap with arXiv:1902.07593, arXiv:1812.10788
- University of Qom Iran (Islamic Republic of)
- University of Genoa Italy
- Qom University of Technology Iran (Islamic Republic of)
- Shiraz University Iran (Islamic Republic of)
FOS: Computer and information sciences, distributed optimization; fuzzy c-means clustering; hyperspectral data; least mean square strategy; spectral unmixing, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
FOS: Computer and information sciences, distributed optimization; fuzzy c-means clustering; hyperspectral data; least mean square strategy; spectral unmixing, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
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