TY - JOUR
T1 - Interactive Visualization of Hyperspectral Images Based on Neural Networks
AU - Zhu, Feiyu
AU - Pan, Yu
AU - Gao, Tian
AU - Walia, Harkamal
AU - Yu, Hongfeng
N1 - Publisher Copyright:
© 1981-2012 IEEE.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - It is challenging to interpret hyperspectral images in an intuitive and meaningful way, as they usually contain hundreds of dimensions. We develop a visualization tool for hyperspectral images based on neural networks, which allows a user to specify the regions of interest, select bands of interest, and obtain hyperspectral classification results in a scatterplot generated from hyperspectral features. A cascade neural network is trained to generate a scatterplot that matches the cluster centers labeled by the user. The inferred scatterplot not only shows the clusters of points, but also reveals relationships of substances. The trained neural network can be reused for time-varying hyperspectral data analysis without retraining. Our visualization solution can keep domain experts in the analytical loop and provide an intuitive analysis of hyperspectral images while identifying different substances, which are difficult to be realized using existing hyperspectral image analysis techniques.
AB - It is challenging to interpret hyperspectral images in an intuitive and meaningful way, as they usually contain hundreds of dimensions. We develop a visualization tool for hyperspectral images based on neural networks, which allows a user to specify the regions of interest, select bands of interest, and obtain hyperspectral classification results in a scatterplot generated from hyperspectral features. A cascade neural network is trained to generate a scatterplot that matches the cluster centers labeled by the user. The inferred scatterplot not only shows the clusters of points, but also reveals relationships of substances. The trained neural network can be reused for time-varying hyperspectral data analysis without retraining. Our visualization solution can keep domain experts in the analytical loop and provide an intuitive analysis of hyperspectral images while identifying different substances, which are difficult to be realized using existing hyperspectral image analysis techniques.
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U2 - 10.1109/MCG.2021.3097730
DO - 10.1109/MCG.2021.3097730
M3 - Article
C2 - 34280091
AN - SCOPUS:85111022876
SN - 0272-1716
VL - 41
SP - 57
EP - 66
JO - IEEE Computer Graphics and Applications
JF - IEEE Computer Graphics and Applications
IS - 5
M1 - 9490338
ER -