Title :
Remotely sensed data analysis using two neural networks and its application to land cover mapping
Author :
Murai, Hiroshi ; Omatu, Sigeru ; Oe, Shunichiro
Author_Institution :
Shikoku Univ., Tokushima, Japan
Abstract :
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
Keywords :
feedforward neural nets; geophysical signal processing; geophysical techniques; geophysics computing; image classification; remote sensing; self-organising feature maps; BPM; Kohonen self-organization feature map; Kohonen´s self-organization feature mapping preprocessor; PCA; SOM; data analysis; feedforward neural net; geophysical measurement technique; hybrid system; image classification; image processing; input pattern; land cover mapping; land surface; multilayered neural net; neural net; neural network; principal component analysis; remote sensing; terrain mapping; Cities and towns; Data analysis; Data mining; Data preprocessing; Multi-layer neural network; Neural networks; Pattern classification; Principal component analysis; Rivers; Training data;
Conference_Titel :
Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4403-0
DOI :
10.1109/IGARSS.1998.702920
