Compared with the traditional pixel-based classification method, the object-oriented classification method can reach a higher accuracy. As the middle entity in the process of information extraction, the object is one of the key factors of the object-oriented classification. The quality of the segmentation is directly related to the image classification accuracy. In this paper, a segmentation approach based on the high precision historical cropland parcels is presented. In this approach, the cropland parcels are considered to be homogeneous or not based on the real-time remote sensing image, then the global contrast index is calculated by each parcel to find the best local segmentation parameters, and the improved result is finally approached. The approach was tested in an agricultural area, and the result shows that: 1) the approach can get a more homogeneous object with stable boundaries; 2) Local segmentation parameters provide a more reasonable result in which the "less-segmentation" phenomenon and the "over-segmentation" phenomenon are effectively eliminated; 3) Automatic selection of local optimal segmentation parameters greatly enhances the objectivity of this approach; 4) Global contrast index is sensitive to the "less-segmentation" phenomenon and the "over-segmentation" phenomenon, so it can lead the segmentation to produce the best result. On the other hand, it can also serve as a good index to evaluate image segmentation in agricultural areas.
XU Meng-jie,CHEN Li,LIU Huan-jin, et al. Research on Multi-spectral Image Segmentation of Agriculture Area Based on High Precision Historical Cropland Parcels[J]. REMOTE SENSING FOR LAND & RESOURCES,
2011, 23(4): 20-25.
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2011,
Vol. 23
