DocumentCode :
2995420
Title :
Object-Oriented Port Detection Based on Mean Shift Segmentation
Author :
Kun, Li ; Ran, Yang ; Qianqing, Qin
Author_Institution :
LIESMARS, Wuhan Univ., Wuhan, China
fYear :
2010
fDate :
25-27 June 2010
Firstpage :
1399
Lastpage :
1402
Abstract :
In this paper, we propose a new method for port detection using remote sensing image. This technique is a combination of image segmentation and object-oriented detection. We use mean shift method to initially segment the remote sensing image. Based on the initial segmentation map we build the spatial adjacency relation of the regions. Significant department has been observed in object-oriented detection. Generally, ports have inherent characteristic of half close region of seawater. Convex polygon created by centers of land region along the coastline. The land region is controlled within a certain range. Port detection is implemented according to water areas within the convex polygon. The advantages of this method are the robustness and its affine invariance with respect to translation, scaling, rotation and skewing. The effectiveness of this algorithm is demonstrated using two remote sensing images.
Keywords :
cartography; feature extraction; image segmentation; object-oriented methods; remote sensing; seawater; affine invariance; coastline; convex polygon; half close region; image segmentation; initial segmentation map; land region; mean shift segmentation; object oriented port detection; remote sensing image; seawater; spatial adjacency relation; water areas; Algorithm design and analysis; Feature extraction; Image segmentation; Kernel; Pixel; Remote sensing; Target recognition; meanshift segment; object-oriented; port detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
Type :
conf
DOI :
10.1109/iCECE.2010.346
Filename :
5630639
Link To Document :
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