A Probabilistic Framework for the Segmentation of Remote Sensing Imagery

doi:10.13474/j.cnki.11-2246.2018.0102

Abstract

Abstract:

Image segmentation method based on region growing suffers the problem of selecting appropriate growing seeds and segmentation parameters.A segmentation method based on a novel kind of region-merging probability measure is proposed to solve the problem in this paper.First,an initial segmentation is achieved by marker-based watershed transformation and simple region merging.Statistic,context and shape information can be calculated from the initial segmentation regions.Then a scale-independent merging probability of neighboring regions is calculated by the information based on Bayesian criterion,which is integrated into sequential region merging procedure.It is easier to determine a threshold value for the new probability measure because of its intuitive meaning.And multi-scale objects can be simultaneously segmented in once segmentation,since the probability is independent of scale.Experiment shows a good performance of the method by visual and quantitative analysis and compared with multi-resolution segmentation results of eCognition.

Key words: remote sensing image segmentation, probabilistic framework, regional growth, watershed transformation

CLC Number:

P237

ZHAO Zhan, YAN Li, XIA Wang. A Probabilistic Framework for the Segmentation of Remote Sensing Imagery[J]. 测绘通报, 2018, 0(4): 10-15.

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