A gradient based method for fully constrained least-squares unmixing of hyperspectral images
- Resource Type
- Conference
- Authors
- Chen, Jie; Richard, Cedric; Lanteri, Henri; Theys, Celine; Honeine, Paul
- Source
- 2011 IEEE Statistical Signal Processing Workshop (SSP) Statistical Signal Processing Workshop (SSP), 2011 IEEE. :301-304 Jun, 2011
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Hyperspectral imaging
Materials
Mathematical model
Signal processing algorithms
Equations
Pixel
Hyperspectral imagery
linear unmixing
estimation under constraints
- Language
- ISSN
- 2373-0803
Linear unmixing of hyperspectral images is a popular approach to determine and quantify materials in sensed images. The linear unmixing problem is challenging because the abundances of materials to estimate have to satisfy non-negativity and full-additivity constraints. In this paper, we investigate an iterative algorithm that integrates these two requirements into the coefficient update process. The constraints are satisfied at each iteration without using any extra operations such as projections. Moreover, the mean transient behavior of the weights is analyzed analytically, which has never been seen for other algorithms in hyperspectral image unmixing. Simulation results illustrate the effectiveness of the proposed algorithm and the accuracy of the model.