基于混沌布谷鸟优化的二维Tsallis交叉熵建筑物遥感图像分割

为了进一步提升建筑物遥感图像分割的准确性和运算速度，本文提出了基于混沌布谷鸟优化的二维Tsallis交叉熵的建筑物遥感图像分割方法。首先给出了二维Tsallis交叉熵的阈值选取公式，然后将Logistic混沌映射引入布谷鸟算法，进一步加快布谷鸟算法的收敛速度，最后通过该混沌布谷鸟算法优化基于二维Tsallis交叉熵的阈值寻找过程，并以得到的最优阈值分割建筑物遥感图像。大量实验结果表明，与二维倒数交叉熵法、二维Tsallis熵法、基于混沌粒子群优化的二维Tsallis灰度熵法等方法相比较，本文方法分割的目标更为准确，细节更为清晰，且运算时间更短。

Thresholding for Remote Sensing Images of Building Based on Two-Dimensional Tsallis Cross Entropy Using Chaotic Cuckoo Search Optimization

In order to improve the accuracy and running speed of segmentation of building remote sensing images, a threshold segmentation method based on the 2-D Tsallis cross entropy image threshold selection using chaotic cuckoo search optimization is proposed. Firstly, the formula of 2-D Tsallis cross entropy threshold selection based on the histogram is derived. Next, in order to improve the convergence rate, logistic chaotic map is applied to the cuckoo search algorithm. Finally, the proposed chaotic cuckoo search algorithm is utilized for precise optimization of thresholds based on the 2-D Tsallis cross entropy, so as to realize the threshold segmentation of building remote sensing images with optimal threshold. A large number of experiments show that, compared with 2-D reciprocal cross entropy thresholding method, 2-D Tsallis entropy thresholding method, 2-D Tsallis gray entropy thresholding method based on chaotic particle swarm optimization and so on, the objects in the images segmented by the proposed method are more accurate, the details are more explicit, in addition, its running time is shorter.