Interactive Visualization of Hyperspectral Images Based on Neural Networks

Feiyu Zhu, Yu Pan, Tian Gao, Harkamal Walia, Hongfeng Yu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number9490338
Pages (from-to)57-66
Number of pages10
JournalIEEE Computer Graphics and Applications
Volume41
Issue number5
DOIs
StatePublished - Sep 1 2021

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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