| Paper ID | IVMSP-14.2 |
| Paper Title |
AN ADMM BASED NETWORK FOR HYPERSPECTRAL UNMIXING TASKS |
| Authors |
Chao Zhou, Miguel R.D. Rodrigues, University College London, United Kingdom |
| Session | IVMSP-14: Hyperspectral Imaging |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we use algorithm unrolling approaches in order to design a new neural network structure applicable to hyperspectral unmixing challenges. In particular, building upon a constrained sparse regression formulation of the underlying unmixing problem, we unroll an ADMM solver onto a neural network architecture that can be used to deliver the abundances of different (known) endmembers given a reflectance spectrum. Our proposed network -- which can be readily trained using standard supervised learning procedures -- is shown to possess a richer structure consisting of various skip connections and shortcuts than other competing architectures. Moreover, our proposed network also delivers state-of-the-art unmixing performance compared to competing methods. |
