Title :
Research on remote sensing classification based on improved Kohonen neural network
Author :
Luo, Xiaobo ; Liu, Qinhuo ; Liu, Qiang ; Xia, Ying
Author_Institution :
Inst. of Remote Sensing Applic., Chinese Acad. of Sci., Beijing, China
Abstract :
In this paper, we used max-min distance method to determine Kohonen Network´ initial weights, as well as introduced energy function as the convergence condition of network, and as such improved Kohonen network´ unsupervised-learning algorithm. We then used the improved Kohonen´ learning algorithm combined with penalty formula to carry out supervised classification of remote sensing data. Experiments showed that higher accuracy resulted with this improved algorithm.
Keywords :
pattern classification; remote sensing; self-organising feature maps; unsupervised learning; Kohonen neural network; energy function; max-min distance method; penalty formula; remote sensing classification; supervised data classification; unsupervised learning algorithm; Application software; Artificial neural networks; Computer science; Convergence; Detectors; Educational institutions; Neural networks; Neurons; Remote sensing; Unsupervised learning; Kohonen neural network; max-min distance means; supervised classification;
Conference_Titel :
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-6347-3
DOI :
10.1109/ICCET.2010.5485413
