CLC number: TP751
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-03-04
Cited: 0
Clicked: 4500
Citations: Bibtex RefMan EndNote GB/T7714
Cheng-ming Ye, Xin Liu, Hong Xu, Shi-cong Ren, Yao Li, Jonathan Li. Classification of hyperspectral images based on a convolutional neural network and spectral sensitivity[J]. Journal of Zhejiang University Science A, 2020, 21(3): 240-248.
@article{title="Classification of hyperspectral images based on a convolutional neural network and spectral sensitivity",
author="Cheng-ming Ye, Xin Liu, Hong Xu, Shi-cong Ren, Yao Li, Jonathan Li",
journal="Journal of Zhejiang University Science A",
volume="21",
number="3",
pages="240-248",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1900085"
}
%0 Journal Article
%T Classification of hyperspectral images based on a convolutional neural network and spectral sensitivity
%A Cheng-ming Ye
%A Xin Liu
%A Hong Xu
%A Shi-cong Ren
%A Yao Li
%A Jonathan Li
%J Journal of Zhejiang University SCIENCE A
%V 21
%N 3
%P 240-248
%@ 1673-565X
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1900085
TY - JOUR
T1 - Classification of hyperspectral images based on a convolutional neural network and spectral sensitivity
A1 - Cheng-ming Ye
A1 - Xin Liu
A1 - Hong Xu
A1 - Shi-cong Ren
A1 - Yao Li
A1 - Jonathan Li
J0 - Journal of Zhejiang University Science A
VL - 21
IS - 3
SP - 240
EP - 248
%@ 1673-565X
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1900085
Abstract: In recent years, deep learning methods have gradually come to be used in hyperspectral imaging domains. Because of the peculiarity of hyperspectral imaging, a mass of information is contained in the spectral dimensions of hyperspectral images. Also, different objects on a land surface are sensitive to different ranges of wavelength. To achieve higher accuracy in classification, we propose a structure that combines spectral sensitivity with a convolutional neural network by adding spectral weights derived from predicted outcomes before the final classification layer. First, samples are divided into visible light and infrared, with a portion of the samples fed into networks during training. Then, two key parameters, unrecognized rate (δ) and wrongly recognized rate (γ), are calculated from the predicted outcome of the whole scene. Next, the spectral weight, derived from these two parameters, is calculated. Finally, the spectral weight is added and an improved structure is constructed. The improved structure not only combines the features in spatial and spectral dimensions, but also gives spectral sensitivity a primary status. Compared with inputs from the whole spectrum, the improved structure attains a nearly 2% higher prediction accuracy. When applied to public data sets, compared with the whole spectrum, on the average we achieve approximately 1% higher accuracy.
The paper reports the classification of a HSI data set using CNN. The innovation of this work has been using two different weightings for the visible and NIR bands to enhance class predictions. This is similar to the band selection approach. The idea seems to be plausibly good.
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