Hyperspectral unmixing using double reweighted collaborative sparse regression

Yan Li; Shengqian Wang

doi:10.1117/12.2540096

14 August 2019 Hyperspectral unmixing using double reweighted collaborative sparse regression

Yan Li, Shengqian Wang

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117933 (2019) https://doi.org/10.1117/12.2540096
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

An important technique in hyperspectral unmixing is collaborative sparse regression. It improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Now it is well known that introducing weighted factors to enforce sparseness becomes a necessary process in sparse unmixing methods. In this paper, considering the desirable performance of reweighted minimization, a double reweighted collaborative sparse regression (DR-CLSUnSAL) has been utilized. The proposed method enhances the sparsity of abundance factions in both the spectral sparsity (column sparsity of the fractional abundances in the sense) and the spatial sparsity (row sparsity of the fractional abundances in the sense). Then the optimization problem was simply solved by the variable splitting and augmented Lagrangian algorithm. Our experimental results with simulated data sets generated by randomly extracting from the United State Geological Survey(USGS) library demonstrate that the proposed method is an effective and accurate sparse unmixing algorithm compared with other advanced hyperspectral unmixing methods.

Citation Download Citation

Yan Li and Shengqian Wang "Hyperspectral unmixing using double reweighted collaborative sparse regression", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117933 (14 August 2019); https://doi.org/10.1117/12.2540096

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