@inproceedings{3fc16d15465c40bdbf128e537255bcff,
title = "Using input feature information to improve ultraviolet retrieval in neural networks",
abstract = "In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.",
keywords = "Extreme learning machine, Input feature, Neural network, Ultraviolet retrieval",
author = "Zhibin Sun and Chang, \{Ni Bin\} and Wei Gao and Maosi Chen and Melina Zempila",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Remote Sensing and Modeling of Ecosystems for Sustainability XIV 2017 ; Conference date: 09-08-2017",
year = "2017",
doi = "10.1117/12.2274522",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jinnian Wang and Wei Gao and Ni-Bin Chang",
booktitle = "Remote Sensing and Modeling of Ecosystems for Sustainability XIV",
address = "美国",
}