直线截距直方图城区遥感图像多阈值分割

阈值分割简单有效，但现有的单阈值方法对城区图像分割效果不佳，难以取得令人满意的结果。为了快速准确地对城区遥感图像进行分割，本文提出了基于直线截距直方图倒数灰度熵和人工蜂群优化（artificial bee colony optimization, ABC）的多阈值分割方法。首先，给出直线截距直方图的定义并建立城区遥感图像的直线截距直方图；然后，计算该直方图倒数灰度熵的大小，推导出其单阈值选取公式；最后，将其推广到多阈值选取，并利用人工蜂群优化算法，对多个阈值进行快速精确地寻优，以此最终实现城区遥感图像的多阈值分割。实验结果表明，该方法所分割的图像中多目标的形状、边缘更为准确，纹理及细节特征更加清晰，且所需运行时间仅为同类多阈值分割方法的25%，是一种行之有效的城区遥感图像分割方法。

Multi-level thresholding for remote sensing image of urban area based on line intercept histogram

Threshold segmentation is a kind of simple and effective method, however, the existing single-threshold method is hard to realize satisfactory effect in segmenting the images of urban area. In order to segment the remote sensing images of urban area quickly and accurately, a multi-threshold segmentation method based on straight-line intercept histogram, reciprocal grayscale entropy and Artificial Bee Colony (ABC) Optimization was proposed in the paper. Firstly, the straight-line intercept histogram was defined and the straight-line intercept histogram of the urban remote sensing image was established; then the value of the reciprocal grayscale entropy of the histogram was calculated and the single-threshold selection formula was deduced; finally, the application was popularized to multi-threshold selection, ABC Optimization algorithm was utilized for precise optimization of many thresholds, so as to finally realize the multi-threshold segmentation of urban remote sensing images. A large number of experiments show that, the multi-object shape and edge in the images segmented by the method are more accurate, the textures and details are more explicit, in addition, its running time is only 25% of other similar multi-threshold segmentation methods. This is a kind of effective method for segmenting the remote sensing images of urban area.