Hyperspectral Imagery Denoising by Deep Learning With Trainable Nonlinearity Function

Hyperspectral images (HSIs) can describe subtle differences in the spectral signatures of objects, and thus they are effective in a wide array of applications. However, an HSI is inevitably contaminated with some unwanted components like noise resulting in spectral distortion, which significantly de...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2017-11, Vol.14 (11), p.1963-1967
Main Authors: Xie, Weiying, Li, Yunsong
Format: Article
Language:English
Subjects:
Artificial neural networks
Convolution
Cubic neighbor patches
deep convolutional neural network (CNN)
Deep learning
hyperspectral image (HSI) denoising
Hyperspectral imaging
Imagery
Neural networks
Noise
Noise measurement
Noise reduction
Nonlinear systems
Nonlinearity
Spectral signatures
trainable nonlinearity function
Training
Training data
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Summary:Hyperspectral images (HSIs) can describe subtle differences in the spectral signatures of objects, and thus they are effective in a wide array of applications. However, an HSI is inevitably contaminated with some unwanted components like noise resulting in spectral distortion, which significantly decreases the performance of postprocessing. In this letter, a deep stage convolutional neural network (CNN) with trainable nonlinearity functions is applied for the first time to remove noise in HSIs. Besides the fact that the weight and bias matrices are learned from cubic training clean-noisy HSI patches, the nonlinearity functions in each stage are also trainable, which differ from the conventional CNN with a fixed nonlinearity function. Compared with the state-of-the-art HSI denoising methods, the experimental results on both synthetic and real HSIs confirm that the proposed method can obtain a more effective and efficient performance.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2017.2743738