Artificial neural network applications on remotely sensed imagery

Author

Das, Kaushik ; Ding, Qin ; Perrizo, William

Author_Institution

Dept. of Comput. Sci., North Dakota State Univ., Fargo, ND, USA

Volume

3

fYear

2001

fDate

2001

Firstpage

510

Abstract

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

Keywords

agriculture; distributed object management; hydrological techniques; image processing; pattern clustering; remote sensing; self-organising feature maps; soil; terrain mapping; unsupervised learning; wavelet transforms; CORBA; SOM; artificial neural network applications; clustering; data pre-processing; distributed system; knowledge extraction; moisture; nitrate; remotely sensed imagery; self-organizing maps; unsupervised learning; Artificial neural networks; Biological system modeling; Data mining; Moisture; Neural networks; Neurons; Nitrogen; Performance analysis; Self organizing feature maps; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Info-tech and Info-net, 2001. Proceedings. ICII 2001 - Beijing. 2001 International Conferences on

Conference_Location

Beijing

Print_ISBN

0-7803-7010-4

Type

conf

DOI

10.1109/ICII.2001.983108

Filename

983108