Segmentation of high resolution satellite imagery based on mean shift algorithm and morphological operations

Data-driven unsupervised segmentation of high resolution remotely sensed images is a primary step in understanding remotely sensed images. A new fully automatic method to delineate the segments corresponding to objects in high resolution remotely sensed images is introduced. There are extensive methods proposed in the literature which are mainly concentrated on pixel level information. The proposed method combines the structural information extracted by morphological processing with feature space analysis based on mean shift algorithm. The spectral and spatial bandwidth parameters of mean shift are adaptively determined by exploiting differential morphological profile (DMP), Spectral bandwidth is determined in relation to the first maximum value of DMP at each pixel and spatial bandwidth is determined by the corresponding index in DMP. in this method there is also no need to specify initially the maximum size of the structuring element for the morphological processes. By the use of mean shift filtering, the feature space points are grouped together which are close to each other both in the range of spatial and spectral bandwidths. The proposed method is applied on panchromatic high resolution QuickBird satellite images taken from urban areas. The results we obtained appear to be effective in terms of segmentation and combining the spectral and spatial information to extract more precise and more meaningful objects compared to fixed bandwidth mean shift segmentation. © 2009 SPIE.


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Citation Formats
Ö. Aytekin, İ. Ulusoy, and U. Halıcı, “Segmentation of high resolution satellite imagery based on mean shift algorithm and morphological operations,” 2009, vol. 7477, Accessed: 00, 2020. [Online]. Available: