Using input feature information to improve ultraviolet retrieval in neural networks

Zhibin Sun; Ni-Bin Chang; Wei Gao; Maosi Chen; Melina Zempila

doi:10.1117/12.2274522

1 September 2017 Using input feature information to improve ultraviolet retrieval in neural networks

Zhibin Sun, Ni-Bin Chang, Wei Gao, Maosi Chen, Melina Zempila

Proceedings Volume 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV; 1040506 (2017) https://doi.org/10.1117/12.2274522
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Zhibin Sun, Ni-Bin Chang, Wei Gao, Maosi Chen, and Melina Zempila "Using input feature information to improve ultraviolet retrieval in neural networks", Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 1040506 (1 September 2017); https://doi.org/10.1117/12.2274522

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