UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Blind Unmixing Using A Double Deep Image Prior

Zhou, Chao; Rodrigues, Miguel RD; (2022) Blind Unmixing Using A Double Deep Image Prior. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. pp. 1665-1669). IEEE: Singapore, Singapore. Green open access

[thumbnail of ICASSP_2022__Blind_Unmixing_using_Double_Deep_Image_Prior.pdf]
Preview
Text
ICASSP_2022__Blind_Unmixing_using_Double_Deep_Image_Prior.pdf - Accepted Version

Download (1MB) | Preview

Abstract

In this paper, we propose a novel network structure to solve the blind hyperspectral unmixing problem using a double Deep Image Prior (DIP). In particular, the blind unmixing problem involves two sub-problems: endmember estimation and abundance estimation. We, therefore, propose two sub-networks, endmember estimation DIP (EDIP) and abundance estimation DIP (ADIP), to generate the estimation of endmembers and estimation of corresponding abundances respectively. The overall network is then constructed by assembling these two sub-networks. The network is trained in an end-to-end manner by minimizing a novel composite loss function. The experiments on synthetic and real datasets show the effectiveness of the proposed method over state-of-art unmixing methods.

Type: Proceedings paper
Title: Blind Unmixing Using A Double Deep Image Prior
Event: 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Location: Singapore, SINGAPORE
Dates: 22 May 2022 - 27 May 2022
ISBN-13: 9781665405409
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICASSP43922.2022.9747545
Publisher version: https://doi.org/10.1109/ICASSP43922.2022.9747545
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Acoustics, blind unmixing, Computer Science, Computer Science, Artificial Intelligence, deep image prior (DIP), Engineering, Engineering, Electrical & Electronic, Hyperspectral unmixing, neural networks, Science & Technology, Technology
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10172094
Downloads since deposit
29Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item