DocumentCode :
2136897
Title :
Advances in the segmentation and compression of multispectral images
Author :
D´Elia, Ciro ; Poggi, Giovannia ; Scarpa, Giuseppppe
Author_Institution :
Dipt. di Ingegneria Elettronica. e delle Telecomunicazioni, Universita di Napoli Federico II, Naples, Italy
Volume :
6
fYear :
2001
fDate :
2001
Firstpage :
2671
Abstract :
Presents a new low-complexity technique for the segmentation of multispectral images, based on the use of a tree-structured Markov random field model. The image is associated with a binary tree, and is segmented recursively through a sequence of local splits based on a maximum a posteriori probability rule. To improve the reliability of the process, merging of nodes is now considered besides splitting, so as to allow for the re-shaping of incorrect region boundaries. Experimental results show that the new algorithm increases the fitness of the segmentation to the actual features of the image
Keywords :
data compression; image coding; image segmentation; remote sensing; binary tree; compression; maximum a posteriori probability rule; merging nodes; multispectral images; recursive segmentation; region boundaries; splitting; technique; tree-structured Markov random field model; Image coding; Image segmentation; Multispectral imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
Type :
conf
DOI :
10.1109/IGARSS.2001.978125
Filename :
978125
Link To Document :
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