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

doi:10.13474/j.cnki.11-2246.2021.0037

Abstract

Abstract: 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 pre-judgment. 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.

Key words: image segmentation, histogram, region growing, merge, pre-judgment

CLC Number:

P237

LIU Siyan, LI Ling, TE Rigen, LI Zhuqiang, MA Jingyu, ZHU Ruifei. Threshold segmentation algorithm based on histogram region growing for remote sensing images[J]. Bulletin of Surveying and Mapping, 2021, 0(2): 25-29.

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