Title
Comparison of advanced neural network architectures for hyperspectral data classification
Abstract
We investigate the performance of two advanced neural network architectures proposed earlier for hyperspectral data classification. While the first architecture uses feature reduction based on the samples of the classes, the second architecture uses a completely unsupervised approach for feature reduction using auto-associative neural networks. The aim of this study is to identify the pros and cons of such multi-level neural network architectures while classifying hyperspectral data.
Year
DOI
Venue
2010
10.1109/WHISPERS.2010.5594919
WHISPERS
Keywords
Field
DocType
data handling,neural net architecture,pattern classification,autoassociative neural networks,feature reduction,hyperspectral data classification,multilevel neural network architectures,class-dependent neural networks,auto-associative neural networks,classification,accuracy,hyperspectral imaging,computer architecture,classification algorithms,artificial neural networks,asphalt,neural network
Data mining,Architecture,Hyperspectral data classification,Computer science,Hyperspectral imaging,Neural net architecture,Artificial intelligence,Statistical classification,Artificial neural network,Group method of data handling,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-8907-7
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Prashanth Reddy Marpu126015.00
Giorgio A. Licciardi2404.82
Paolo Gamba368292.97
Fabio Del Frate450872.43