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My ICIP 2006 Schedule
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Paper Detail
| Paper: | WP-P6.5 |
| Session: | Remote Sensing Imaging and Processing |
| Time: | Wednesday, October 11, 14:20 - 17:00 |
| Presentation: |
Poster |
| Topic: |
Geosciences and Remote Sensing: Multispectral / hyperspectral imaging |
| Title: |
NON-NEGATIVE MAXIMUM LIKELIHOOD ICA FOR BLIND SOURCE SEPARATION OF IMAGES AND SIGNALS WITH APPLICATION TO HYPERSPECTRAL IMAGE SUBPIXEL DEMIXING. |
| Authors: |
Tariq Bakir; Harris | | | | | Adrian Peter; Harris | | | | | Ron Riley; Harris | | | | | Jay Hackett; Harris | | |
| Abstract: |
The use of independent component analysis (ICA) methods for blind source separation of signals and images has been demonstrated in many applications and publications. While many ICA based algorithms for source separation exist, few impose physical constraints on the recovered independent components and the mixing matrix. Of particular interest is the non-negativity of the recovered independent components and the recovered mixing matrix. Such constraints are important for example when trying to do subpixel demixing on hyperspectral images. In this article, we propose a constrained non-negative maximum-likelihood ICA (CNML-ICA) algorithm that overcomes the limitations of some existing non-negative ICA methods. |
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