Remotely sensed data analysis using two neural networks and its application to land cover mapping

Remotely sensed data analysis using two neural networks and its application to land cover mapping

In recent works, the authors have proposed a hybrid system using a Kohonen's self-organization feature mapping preprocessor (SOM) and a multi-layered neural network processor (BPM) to analyze remotely sensed data, and demonstrated the applicability of SOM preprocessor by a principal component a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Murai, H., Omatu, S., Oe, S.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Cities and towns
Data analysis
Data mining
Data preprocessing
Multi-layer neural network
Neural networks
Pattern classification
Principal component analysis
Rivers
Training data
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In recent works, the authors have proposed a hybrid system using a Kohonen's self-organization feature mapping preprocessor (SOM) and a multi-layered neural network processor (BPM) to analyze remotely sensed data, and demonstrated the applicability of SOM preprocessor by a principal component analysis (PCA). In the present paper, the authors empirically examine the significance of the principal components for the input pattern.
DOI:10.1109/IGARSS.1998.702920