Title :
Graph based neural self-organization in analyzing remotely sensed images
Author_Institution :
Dept. of Photogrammetry & Geoinf., Budapest Univ. of Technol. & Econ., Hungary
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN :
0-7803-7929-2
DOI :
10.1109/IGARSS.2003.1295320
