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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Vydáno v:IEEE International Geoscience and Remote Sensing Symposium proceedings s. 9139 - 9142
Hlavní autoři: Zong, Hongru, Liu, Jianjun
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 07.07.2024
Témata:
autocoder
clustering information
Convolution
Data mining
Deep learning
Geoscience and remote sensing
hyperspectral blind unmixing
Hyperspectral imaging
Task analysis
ISSN:2153-7003
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Popis
Shrnutí: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