Surface Temperature Estimation from Airborne Imagery Using Neural Network Model

Autor:	Akihiro Nakamura, John Bosco Njoroge, Yukihiro Morimoto
Rok vydání:	2000
Předmět:	Surface (mathematics) Geography Artificial neural network Linear regression Multispectral image Radiance Regression analysis Interval (mathematics) Residual Remote sensing
Zdroj:	IFAC Proceedings Volumes. 33:145-149
ISSN:	1474-6670
DOI:	10.1016/s1474-6670(17)36767-8
Popis:	Comparisons for surface temperature estimation from radiance received by airborne multispectral sensor (MSS) were made between linear regression, multiple regression and back propagation neural network model. Data of upwelling radiance recorded at morning (AM) and afternoon (PM) were applied for analysis. Based on ± 10% residual error interval, a neural network trained with separate AM and PM data sets attained 87.5% prediction of surface temperature compared with 50% by conventional models but attained only 62.5% prediction for AM data when trained with combined data sets. Training with separate data gave more uniform prediction of surface temperature, out-performing conventional models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::87ff8111036ff7121b488ddb3003a0a6 https://doi.org/10.1016/s1474-6670(17)36767-8 Zobrazit plný text záznamu