DocumentCode :
2281099
Title :
The use of morphological profiles in classification of data from urban areas
Author :
Benediktsson, Jon Atli ; Arnason, Kolbeinn ; Pesaresi, Martino
Author_Institution :
Dept. of Electr. & Comput. Eng., Iceland Univ., Reykjavik, Iceland
fYear :
2001
fDate :
2001
Firstpage :
30
Lastpage :
34
Abstract :
Classification of panchromatic IKONOS data from an urban area in Reykjavik, Iceland is investigated. It is well known that conventional classification algorithms have difficulty classifying high resolution data from urban areas. Therefore, in the paper, an approach based on morphological preprocessing is applied. The approach is in two steps. First, the morphological composition of geodesic opening and closing operations of different sizes is used in order to build a morphological profile. Although, the original panchromatic data is only of one data channel, the use of the composition operations gives additional data channels, which may contain redundancies. Therefore, in the second step, a neural classifier with pruning capabilities is used to reduce the number of data channels and classify the data. Several compositions of morphological opening and closing transforms where used to obtain a stack of data using differently sized structure elements
Keywords :
image classification; mathematical morphology; neural nets; terrain mapping; IKONOS data; Iceland; Reykjavik; classification; data channels; geodesic closing operations; geodesic opening operations; high resolution data; morphological composition; morphological profiles; neural classifier; panchromatic data; pruning capabilities; urban areas; Classification algorithms; Conferences; Electronic mail; Filtering; Filters; Image reconstruction; Neural networks; Remote sensing; Shape; Urban areas;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Remote Sensing and Data Fusion over Urban Areas, IEEE/ISPRS Joint Workshop 2001
Conference_Location :
Rome
Print_ISBN :
0-7803-7059-7
Type :
conf
DOI :
10.1109/DFUA.2001.985720
Filename :
985720
Link To Document :
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