Title :
Remote Sensing Image in Mining Area Classification Based on LVQ2 Neural Network
Author :
Hu, Youjian ; Luo, Hongxia
Author_Institution :
Fac. of Inf. Enginerring, China Univ. of Geosci. (Wuhan) CUG, Wuhan, China
Abstract :
The remote sensing shows a widest perspective for land reclamation in mining areas. Based on how to improve the classification accuracy of mine image, we did some classification researchs with LVQ2 neural network. The proposed method had been applied to the aerial image of Heng country, Guangxi Province. The total classification accuracy was 72%, comparing with the minimum distance method increased by 9%.
Keywords :
geophysical image processing; image classification; learning (artificial intelligence); mining; neural nets; remote sensing; vector quantisation; LVQ2 neural network; aerial image; classification accuracy; land reclamation; mine image; minimum distance method; mining area classification; remote sensing image; Accuracy; Artificial neural networks; Classification algorithms; Geology; Image classification; Remote sensing; Signal processing algorithms; LVQ2; mining area; neural network; remote sensing image classification;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.382
