Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology
Date
2011-10-01
Author
AYTEKİN, orsan
Ulusoy, İlkay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
0
views
0
downloads
The morphological profile (MP) and differential morphological profile (DMP) have been used extensively to acquire spatial information to be used in the segmentation of very high resolution (VHR) remotely sensed images. In most of the previous approaches, the maxima of the MP and DMP were investigated to estimate the best representative scale in the spatial domain for the pixel under consideration. Then, the object type (i.e. dark, bright or flat) was estimated based on the location of the maximum. Finally, the image segmentation was performed using the scale and type information as features. This approach usually causes over-segmentation. In this study, we also investigate the relevance of the DMP and the meaningful object types underlying the pixel of interest, however, instead of the maxima of the DMP, the type information is estimated using the whole DMP which is weighted by a weight function. Thus, the scale is not estimated directly but used indirectly in the estimation of the characteristic type for the object to which the pixel belongs. Then, the pixels are clustered based on their types only. The method has been applied to panchromatic high resolution QuickBird satellite images of the city of Ankara, Turkey. The results of the method were compared with previous studies and the proposed method seems to segment the images more precisely and semantically than the previous approaches.
Subject Keywords
Signal Processing
,
Software
,
Artificial Intelligence
,
Computer Vision and Pattern Recognition
URI
https://hdl.handle.net/11511/41783
Journal
PATTERN RECOGNITION LETTERS
DOI
https://doi.org/10.1016/j.patrec.2011.06.024
Collections
Department of Electrical and Electronics Engineering, Article