This study on the vector C-V model and hyperspectral remote sensing image aims is to segment a hyperspectral remote sensing image. A hyperspectral remote sensing image contains not only general two-dimensional image spatial information but also have one-dimensional spectrum information. Thus

traditional methods of two-dimensional image segmentation are unsuitable for hyperspectral remote sensing images. To solve this problem

we propose a hypespectral remote sensing image vector C-V model segmentation method based on band selection

which can deal with the multiband images at the same time. Method First

bands of goals and backgrounds contrast that exhibit a significant contrast were chosen based on the band correlation coefficient. Then

the greater relevance band-by-band correlation coefficient was removed

and a new band combination was formed. Finally

a hyperspectral remote sensing image vector matrix was built. On these basis

we can construct a vector C-V model that takes full advantage of this vector matrix while introducing a gradient-based edge guide function. Result Numerical experiments were conducted on HYPERION data

and these experiments were compared with the traditional C-V model and Wang & Jin method. The result shows that the proposed method can immediately segment a hyperspectal remote sensing image effectively

and it not only has a lower fasepositive ratio and false-negative ratio but also a smaller error ratio. These results prove that the segmentation of the proposed model is more effective than that of the traditional C-V model and Wang & Jin method. In sum

compared with the traditional C-V model and Wang & Jin method

the proposed model improved the segmentation speed and accuracy. Conclusion The proposed model does not retain the traits of conventional C-V model

which is based on the regional information. Rather

it increased the ability of capture the boundary of the target in heterogeneous regions and complex background by using image edge details. However

the method has some shortcomings. For instance

it uses only the gray level information without the spectral information of hyperstpectral remote image during the process of segmentation

which causes a small amount of error in its results. Using the combination of spatial information and spectral information effectively in the process of the segmentation is a problem that needs further research.