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Open AccessArticle

Scattering Transform Framework for Unmixing of Hyperspectral Data

1
Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China
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School of Electrical, Computer and Telecommunication Engineering, University of Wollongong, Wollongong 2500, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2868; https://doi.org/10.3390/rs11232868
Received: 17 October 2019 / Revised: 20 November 2019 / Accepted: 26 November 2019 / Published: 2 December 2019
The scattering transform, which applies multiple convolutions using known filters targeting different scales of time or frequency, has a strong similarity to the structure of convolution neural networks (CNNs), without requiring training to learn the convolution filters, and has been used for hyperspectral image classification in recent research. This paper investigates the application of the scattering transform framework to hyperspectral unmixing (STFHU). While state-of-the-art research on unmixing hyperspectral data utilizing scattering transforms is limited, the proposed end-to-end method applies pixel-based scattering transforms and preliminary three-dimensional (3D) scattering transforms to hyperspectral images in the remote sensing scenario to extract feature vectors, which are then trained by employing the regression model based on the k-nearest neighbor (k-NN) to estimate the abundance of maps of endmembers. Experiments compare performances of the proposed algorithm with a series of existing methods in quantitative terms based on both synthetic data and real-world hyperspectral datasets. Results indicate that the proposed approach is more robust to additive noise, which is suppressed by utilizing the rich information in both high-frequency and low-frequency components represented by the scattering transform. Furthermore, the proposed method achieves higher accuracy for unmixing using the same amount of training data with all comparative approaches, while achieving equivalent performance to the best performing CNN method but using much less training data. View Full-Text
Keywords: hyperspectral image; scattering transform; spectral unmixing; k-NN regressor; high-level information; end-to-end hyperspectral image; scattering transform; spectral unmixing; k-NN regressor; high-level information; end-to-end
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MDPI and ACS Style

Zeng, Y.; Ritz, C.; Zhao, J.; Lan, J. Scattering Transform Framework for Unmixing of Hyperspectral Data. Remote Sens. 2019, 11, 2868.

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