@inproceedings{2f5c4f1896ef4095a9155d3ea4127724,
title = "Visual Analysis for Multi-Spectral Images Comparisons",
abstract = "The analysis for images helps people to gain insights by extracting the inner features and variances between them. However, it is hard to analyze the underlying events further without users participation. We proposes a visual analytic system based on collaborative tagging techniques to allow users to identify features and changes from multi-spectral images. We evaluate our system with mini challenge 3 of VAST Challenge 2017. The exploration results validate the efficiency and effectiveness of our system.",
author = "Guozheng Li and Shuai Chen and Qiusheng Li and Zhibang Jiang and Yuening Shi and Qiangqiang Liu and Xi Liu and Xiaoru Yuan",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 ; Conference date: 01-10-2017 Through 06-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/VAST.2017.8585456",
language = "English",
series = "2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "191--192",
editor = "Brian Fisher and Shixia Liu and Tobias Schreck",
booktitle = "2017 IEEE Conference on Visual Analytics Science and Technology, VAST 2017 - Proceedings",
address = "United States",
}