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Artificial neural network applications on remotely sensed imagery

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3 Author(s)
K. Das ; Dept. of Comput. Sci., North Dakota State Univ., Fargo, ND, USA ; Qin Ding ; W. Perrizo

Huge amount of remotely sensed imagery data provide the possibility and challenges to extract knowledge from them. In this paper, we propose a model for using artificial neural networks to perform unsupervised learning on remotely sensed imagery. This model generates self-organizing maps (SOM) based on remotely sensed imagery and related data such as yield, nitrate, and moisture. It correlates these maps and projects these output into a SOM. In addition, it uses wavelets for data pre-processing. The model also derives important rules. The entire model is implemented as a distributed system using CORBA. Performance analysis shows the model is efficient and effective for performing clustering on remotely sensed imagery

Published in:

Info-tech and Info-net, 2001. Proceedings. ICII 2001 - Beijing. 2001 International Conferences on  (Volume:3 )

Date of Conference: