Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

Abstract:

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

• Periodical: Applied Mechanics and Materials (Volumes 90-93)
• Pages: 2836-2839
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.90-93.2836
• Online since: September 2011
• Authors: Jian Cui, Dong Ling Ma, Ming Yang Yu, Ying Zhou
• Keywords: Image Object Layer, Mean-Shift, Object-Oriented (OO), Region Growing

References

[1] MING Dong-ping; LUO Jian-cheng; SHEN Zhan-feng; WANG MinM; SEHNG Hao. Research on information extraction and target recognition from high resolution remote sensing image. Science of Surveying and mapping, Issue 3 (2005), pp.18-20.

[2] Li guoyan, Liu xiaozheng, Li jinlan. Research on remote sensing image fusion based on image multiscale analysis. Science of Surveying and Mapping, Issue 1 (2008).

[3] Comaniciu D, Meer P. Mean shift analysis and applications. In Proc. Int Conf Computer Vision, 1999, pp.1197-1203.

[4] Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans on Pattern Analysis and Machine Intelligence. Vol. 24, Issue 5 (2002), pp.603-619.

DOI: 10.1109/34.1000236