A near-rectangle guided segmentation method for remote sensing images of corn field areas

Corn field remote sensing images have a mass of end member spectral variability among marginal land area.When traditional method is used for corn block segmentation , it will produce a number of small corn plot areas at the edge and result in statistical errors of the planting area .According to the distribution characteristics of large corn planting area , an near -rectangle guided segmentation method for remote sensing images in corn field areas is proposed.First, the SUSAN ( smallest univale segment assimilating nucleus ) operator is used for edge detection from GF-1 fusion images.Then, according to the relationship between closed area and external near rectangular, the near rectangle-guided correlation function is built .At last, the near-rectangle guided threshold function is introduced into the graph -based segmentation algorithm to implement the field parcel segmentation of a specific shape .The results were compared with the graph -based segmentation algorithm , the watershed algorithm and the artificial interpretation sample . It is shown that the method proposed in this paper is effective in distinguishing different features , and the negative impact resulting from the endmember spectral variability can be reduced.The segmentation results are more in line with the actual characteristics of corn distribution , conforming with the actual statistics of the corn field area .