Dual-Branch Autoencoder with Clustering Information for Hyperspectral Blind Unmixing

Deep learning methods, especially autoencoder-based methods, have become increasingly prevalent in the domain of hyperspectral blind unmixing. However, many models still have a limitation, that is, they cannot fully extract spatial and spectral information from hyperspectral images. In this paper, a...

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Bibliographic Details
Published in:IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 9139 - 9142
Main Authors: Zong, Hongru, Liu, Jianjun
Format: Conference Proceeding
Language:English
Published: IEEE 07.07.2024
Subjects:
autocoder
clustering information
Convolution
Data mining
Deep learning
Geoscience and remote sensing
hyperspectral blind unmixing
Hyperspectral imaging
Task analysis
ISSN:2153-7003
Online Access:Get full text
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Summary:Deep learning methods, especially autoencoder-based methods, have become increasingly prevalent in the domain of hyperspectral blind unmixing. However, many models still have a limitation, that is, they cannot fully extract spatial and spectral information from hyperspectral images. In this paper, a dual-branch autoencoder with clustering information is proposed called DBA-CIU, which can fully utilize the spatial and spectral information of hyperspectral and add a priori clustering information in the hyperspectral image. First, we design a multi-scale convolution to extract spatial information and a band-wise split convolution to extract spectral information. Second, embedding prior clustering information of the hyperspectral image in two branches enhances information utilization. Lastly, sparse regularization is applied to the abundances obtained from the former branch. Experimental results validate the superior competitiveness of our proposed method over alternative approaches in the task of unmixing.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10642094