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.
关键词
高光谱遥感影像分割矢量C-V模型边缘引导函数
Keywords
hyperspectral imageimage segmentationvector C-V modeledge guide function