DocumentCode
411287
Title
Graph based neural self-organization in analyzing remotely sensed images
Author
Barsi, Arpad
Author_Institution
Dept. of Photogrammetry & Geoinf., Budapest Univ. of Technol. & Econ., Hungary
Volume
6
fYear
2003
fDate
21-25 July 2003
Firstpage
3937
Abstract
The Self-Organizing Neuron Graph (SONG) algorithm generalizes the Kohonen feature maps. The technique is proved in analyzing different aerial and satellite images. The base of the method is a given graph; its neurons detect the positions of the data points (pixels), which are derived by image processing functions.
Keywords
geophysical signal processing; graphs; image processing; terrain mapping; Kohonen feature maps; data points; graph based neural self-organization; image processing; pixels; remotely sensed images; satellite images; self-organizing neuron graph algorithm; Associative memory; Biological system modeling; Books; Image analysis; Image processing; Neurons; Pixel; Satellites; Subspace constraints; Topology;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN
0-7803-7929-2
Type
conf
DOI
10.1109/IGARSS.2003.1295320
Filename
1295320
Link To Document