基于Tsallis交叉熵快速迭代的河流遥感图像分割

为了使河流遥感图像分割的精度和速度进一步提高，本文提出了一种基于二维Tsallis交叉熵快速迭代的河流遥感图像分割方法。鉴于现有的Tsallis交叉熵阈值法运算效率不够高，首先提出了一维Tsallis交叉熵阈值选取的快速迭代算法；然后导出了基于灰度级—邻域平均灰度级直方图的Tsallis交叉熵阈值选取公式，以进一步提高分割精度，并采用递推方式计算阈值选取准则函数中的中间变量，避免其重复运算，加快运算速度；最后，提出了二维Tsallis交叉熵阈值选取的快速迭代算法，推导出相应的公式，大大减少了运算量。大量实验结果表明，与近年来提出的4种阈值分割方法相比，本文方法在对河流遥感图像的分割效果及运行时间上均有明显优势，是河流检测与类型识别系统中可选择的一种快速有效的分割方法。 

Thresholding for Remote Sensing Images of Rivers Based on a Fast Iterative Algorithm of Two-Dimensional Tsallis Cross Entropy

To further improve the accuracy and speed of remote sensing image segmentation of rivers, a segmentation method for remote sensing images of rivers is proposed, based on a fast iterative algorithm of two-dimensional Tsallis cross entropy. Firstly, a fast iterative algorithm for threshold selection using one-dimensional Tsallis cross entropy is proposed since the existing Tsallis cross entropy thresholding is not computationally efficient enough. Then the two-dimensional Tsallis cross entropy threshold selection formulae based on gray level-neighborhood average gray level histogram are derived to further improve the segmentation accuracy. In addition, recursive algorithms are adopted to calculate the intermediate variables involved in criterion function to avoid their repetitive computation. As a result, the calculating speed is improved. Finally, a fast iterative algorithm for threshold selection using two-dimensional Tsallis cross entropy is proposed, and the corresponding algorithmic formulae are derived. Thus the amount of calculation is greatly reduced. A large number of experimental results show that, compared with four recent thresholding methods, the proposed method has obvious advantages in segmentation results for remote sensing images of rivers and algorithmic running time. It is a fast and effective segmentation method which can be used in river detection and classification system.