基于直方图区域生长的遥感图像阈值分割算法

Threshold segmentation algorithm based on histogram region growing for remote sensing images

传统阈值分割算法从单阈值扩展到多阈值的过程中,时间复杂度会大幅度增加,并且由于遥感图像信息复杂,会导致分割效果降低。为了解决这些问题,本文提出了基于直方图区域生长的遥感图像阈值分割算法。在本文算法中,每一个灰度级均作为1个初始阈值,用256个阈值将直方图分割成256个原始小区域。为了减少阈值数目,本文将小区域合并成大区域,每一次合并都可视为一次区域的生长。在每次生长过程中,选取熵值H最小的区域作为直方图各区域中的主区域,并通过本文提出的预匹配策略将其与相邻区域合并。每一次区域生长后,阈值数目均减少1个。在整个过程中,最多只需要生长255次。算法的时间复杂度稳定在O(L)级别。最后通过单阈值和多阈值试验证明本文算法在运行时间和分割精度上均具有优势。

In the process of traditional thresholding algorithm from single-threshold to multi-threshold,the time complexity will increase greatly,and because of the complexity of remote sensing image information,the segmentation effect will be reduced.This paper creatively proposes the idea based on histogram region growing for remote sensing images.Each gray level is regarded as a threshold,so that the histogram is divided into 256 original small regions by the 256 thresholds.For reducing the number of thresholds,small regions are merged into large regions.Each merging can be regarded as the growth of a region.In each growth,the region with the smallest entropy H is selected as the main one in all regions of the histogram,and then it is merged with the adjacent region by the way of prejudgment.After growing,the number of thresholds decreases.In the whole process,the growth times are only 255 at most,and the time complexity is stable at O(L).In this paper,single-threshold and multi-threshold experiments show that the algorithm has high accuracy in segmentation results,and has advantages in run time.