引入松弛因子的高分辨率遥感影像自动多层次分割

针对当前高分辨率遥感影像多层次分割尺度参数设置缺少理论框架支持、人为因素影响较多等缺点，提出一种引入松弛因子的高分辨率遥感影像自动多层次分割方法。该方法利用1个松弛因子调节引导区域对象合并的异质性值大小，通过控制每次递归合并区域的对象个数，提高了整体分割的速度；以区域对象间异质性平均值作为基数，引入另一个松弛因子控制分割过程中层次输出的尺度参数，使整个分割过程自动得到不同尺度的多层次分割结果。实验结果表明，该方法具有较高的分割质量，能够满足遥感影像分析及地物提取的精度要求，并且减少了人为因素影响，提高了自动化程度。但是，对于复杂图像内容的地物目标边界处理和减少狭长区域对象的出现还需要进一步深入研究和实践。

Automated hierarchical segmentation of high-resolution remote sensing imagery with introduced relaxation factors

This paper proposes a new automated hierarchical segmentation method with introduced relaxation factors for processing high-resolution remote sensing imagery, which aims to provide a theoretical framework in setting the scale parameters and reducing the influence of human factors. The first relaxation factor is used to adjust the heterogeneity between the image-objects to be merged, thus improving the speed of the entire segmentation by controlling the number of image-objects in each recursive merging. With the mean of the heterogeneity between image-objects taken as the cardinality, the second relaxation factor is introduced to control the scaling parameter of the levels exported in the process of segmentation, automatically producing multi-scale hierarchical segmentation results. The experimental results show that this method produces segmentation with higher quality, which meets the accuracy requirements of further image analysis and geographic object extraction. Other theoretical and practical contributions of this method include reducing the influence of human factors and improving the level of automation in segmentation. Further investigation is still required with respect to processing the boundaries of geographic objects with complex image, and increasing the compactness and smoothness of image-objects.