Landsat 8多光谱数据辅助下的高分影像多尺度分割

为解决高分影像分割的边缘锯齿性明显等问题，本文以黑龙江省伊春市桦皮羌子林场为研究区开展了有无多光谱数据辅助的高分影像分割对比试验。首先，本文设计了多尺度分割算法的相同尺度参数下分割试验，确定了该算法分割GF-2影像时应采用的最佳同质性准则组合参数；然后，基于影像分割对象同质性局部方差变化率反映最优分割尺度的思想，利用ESP2工具找出固定尺度范围内的最优分割尺度范围；最后执行最佳同质性准则组合参数配合下的最优分割尺度范围内各个尺度下的多尺度分割，并采用矢量距离指数、紧密度指数、形状指数对2种分割试验结果进行了评价。结果表明，与GF-2影像独立分割相比，Landsat 8多光谱数据辅助下的GF-2影像分割在矢量距离指数、紧密度指数、形状指数的质量上均有提升，平均提升率分别为8.05%、28.40%、11.76%。

High-resolution image multiresolution segmentation with the aid of Landsat 8 multispectral data

In order to solve the problem of edge sawtooth in high-resolution image segmentation results, this study carries out high-resolution image contrast segmentation experiment with Landsat 8 multi-spectral data assisted or not in Huapiqiangzi forestry form in Yichun City, Heilongjiang Province. Firstly, the paper designs the segmentation experiment under the same scale parameter of multiresolution segmentation algorithm to determine the optimal composition of homogeneity criterion parameters required by the algorithm. Furthermore, based on the idea of local variance (LV) of object heterogeneity within a scene reflects the optimal segmentation scale, scales are found corresponding to obvious peaks of the homogenous local variance variation rate in the specific scale range (100~400, step size is 1) generated by ESP2, which is defined as the optimal segmentation scale range.Finally, the results of the two segmentation are evaluated by vector distance index, compactness index and shape index. The evaluation results show that compared with the independent segmentation of GF-2 image, the GF-2 image segmentation assisted by Landsat 8 multispectral data improves the quality of vector distance index, compactness index and shape index, and the average increase rate are 8.05%, 28.40%, 11.76% separately.