Super-resolution mapping of hyperspectral data using Artificial Neural Network and wavelet

2020 ◽  
Vol 20 ◽  
pp. 100374
Author(s):  
Ankur Dixit ◽  
Shefali Agarwal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Super Resolution ◽  
Hyperspectral Data ◽  
Resolution Mapping ◽  
Artificial Neural ◽  
Super Resolution Mapping

scholarly journals SUGARCANE PRODUCTIVITY ESTIMATION THROUGH PROCESSING HYPERSPECTRAL SIGNATURES USING ARTIFICIAL NEURAL NETWORKS

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 177-182
Author(s):  
C. E. Espinosa ◽  
S. Velásquez ◽  
F. L. Hernández
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Net Primary Productivity ◽  
Poultry Manure ◽  
Hyperspectral Data ◽  
Sugarcane Crop ◽  
Artificial Neural ◽  
Hyperspectral Signatures ◽  
Valle Del Cauca ◽  
Sugarcane Productivity

Abstract. This project uses an artificial neural network to calculate the net primary productivity of an organic sugarcane crop in Hatico’s farm, in Cerrito, Valle del Cauca. The pilot scheme used in this project is composed by 6 treatments of nitrogen fertilization based on green manures (poultry manure and cowpea). During the last two crops’ phenological phases, the artificial neural network was provided with hyperspectral data collected in the field. In addition, an exploratory data study was implemented in order to identify anomalous signs related to the light saturation and the curvature geometry. The first network applied was Autoencoder, in order to reduce the dimensionality of the radiometric resolution of the data. The second network applied was Multilayer Perceptron (MLP), to calculate the productivity values of the patches. After having compared the actual productivity values provided by Cenicaña, this project obtained an accuracy of 91.23% in the productivity predictions.

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