Clustered multitask non-negative matrix factorization for spectral unmixing of hyperspectral data

Sara Khoshsokhan; Roozbeh Rajabi; Hadi Zayyani

doi:10.1117/1.JRS.13.026509

31 May 2019 Clustered multitask non-negative matrix factorization for spectral unmixing of hyperspectral data

Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 026509 (May 2019). https://doi.org/10.1117/1.JRS.13.026509

Abstract

The algorithm based on a 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 is 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.

Citation Download Citation

Sara Khoshsokhan, Roozbeh Rajabi, and Hadi Zayyani "Clustered multitask non-negative matrix factorization for spectral unmixing of hyperspectral data," Journal of Applied Remote Sensing 13(2), 026509 (31 May 2019). https://doi.org/10.1117/1.JRS.13.026509

Received: 6 September 2018; Accepted: 10 May 2019; Published: 31 May 2019

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available