Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology

Date

2011-10-01

Author

AYTEKİN, orsan
Ulusoy, İlkay

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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

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Citation Formats

o. AYTEKİN and İ. Ulusoy, “Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology,” PATTERN RECOGNITION LETTERS, pp. 1618–1625, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41783.