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Variable scale Mean-Shift based method for cropland segmentation from high spatial resolution remote sensing images |
| SU Tengfei, ZHANG Shengwei, LI Hongyu |
| College of Water Conservancy and Civil Engineering, Inner Mongolian Agricultural University, Hohhot 010018, China |
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Abstract In order to improve the effect of information extraction from high spatial resolution remote sensing images (HRI) of cropland, the authors put forward a new HRI segmentation algorithm. Due to the fact that the traditional Mean-Shift (MS) segmentation method only uses a global and single scale, and that some variable bandwidth MS only considers spectral information in their scale estimation process, and croplands with various sizes could be hardly extracted in one segmentation result, the authors improved a MS based approach to tackle this problem. The main consideration lies in two aspects: ① A local variable scale parameter estimation method is proposed; ② The model function for local variable scale is established for MS filtering. The proposed approach mainly consists of 3 parts: ① With the objective of comprehensively considering the response variation of different bands, the diagonal scale parameter matrix is adopted in the kernel function of MS filtering, and it is combined with sample point estimation model to derive the iterative function for variable scale MS filtering; ② For the purpose of increasing automation of the proposed method, local spectral variation and edge strength information are utilized to design a new local scale parameter estimation method; ③ For obtaining the final segmentation, the filtering result is used as input for the fractal net evolution approach (FNEA) which is a spatial clustering method. Two scenes of HRI acquired by RapidEye and OrbView3 were employed for experiment, and the results show that the proposed method can optimize the accuracy of cropland HRI segmentation.
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| Keywords
landscape ecological sensitivity
GIS
Kriging interpolation
Yulin
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Issue Date: 15 August 2017
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2017,
Vol. 29
