Title :
Improvement of classification accuracy by 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 the 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 significance of outputs of the SOM preprocessor by a principal component analysis (PCA). In the present paper the authors apply the proposed system on three images taken by three optical sensors, namely, LANDSAT-TM, JERS1-OPS and SPOT2-HRV, to investigate the applicability of the system
Keywords :
feedforward neural nets; geophysical signal processing; geophysical techniques; geophysics computing; image classification; principal component analysis; self-organising feature maps; terrain mapping; JERS1-OPS; Kohonen´s self-organization feature mapping preprocessor; LANDSAT-TM; SPOT2-HRV; accuracy; feedforward neural net; geophysical measurement technique; image classification; image processing; land cover mapping; land surface; multi-layered neural network processor; neural net; neural network; optical imaging; principal component analysis; remote sensing; terrain mapping; Cities and towns; Data mining; Data preprocessing; Multi-layer neural network; Neural networks; Pattern classification; Principal component analysis; Remote sensing; Satellites; Training data;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-6359-0
DOI :
10.1109/IGARSS.2000.861671
