Adversarial Autoencoder Network for Hyperspectral Unmixing

Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e., abundances), has become a major preprocessing technique for the hyperspectral image analysis. Since the unmixing procedure can be explained a...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 34; H. 8; S. 4555 - 4569
Hauptverfasser: Jin, Qiwen, Ma, Yong, Fan, Fan, Huang, Jun, Mei, Xiaoguang, Ma, Jiayi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Abundance
Adaptation models
Adversarial autoencoder network (AAENet)
Algorithms
autoencoder (AE)
Data models
deep learning
Generative adversarial networks
hyperspectral image (HSI)
Hyperspectral imaging
hyperspectral unmixing
Image analysis
Image processing
Pixels
Robustness
Spatial data
Task analysis
Training
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e., abundances), has become a major preprocessing technique for the hyperspectral image analysis. Since the unmixing procedure can be explained as finding a set of low-dimensional representations that reconstruct the data with their corresponding bases, autoencoders (AEs) have been effectively designed to address unsupervised SU problems. However, their ability to exploit the prior properties remains limited, and noise and initialization conditions will greatly affect the performance of unmixing. In this article, we propose a novel technique network for unsupervised unmixing which is based on the adversarial AE, termed as adversarial autoencoder network (AAENet), to address the above problems. First, the image to be unmixed is assumed to be partitioned into homogeneous regions. Then, considering the spatial correlation between local pixels, the pixels in the same region are assumed to share the same statistical properties (means and covariances) and abundance can be modeled to follow an appropriate prior distribution. Then the adversarial training procedure is adapted to transfer the spatial information into the network. By matching the aggregated posterior of the abundance with a certain prior distribution to correct the weight of unmixing, the proposed AAENet exhibits a more accurate and interpretable unmixing performance. Compared with the traditional AE method, our approach can greatly enhance the performance and robustness of the model by using the adversarial procedure and adding the abundance prior to the framework. The experiments on both the simulated and real hyperspectral data demonstrate that the proposed algorithm can outperform the other state-of-the-art methods.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3114203