Sensitivity Analysis on Performance of Different Unsupervised Threshold Selection Methods in Hyperspectral Change Detection

This paper investigated the performance of different automatic binary threshold selection methods on hyperspectral change detection. For this purpose, 10 recent and most common algorithm for binary threshold selection implemented namely and evaluated. To evaluate these methods first, the sub-space based hyperspectral change detection method applied on the multi-temporal hyperspectral dataset. In the second part, the gray level change map converts to binary change map by mentioned thresholding methods. In this work, real-world hyperspectral dataset utilized to evaluate the related performance of threshold selection methods. The results show that Active-Contour method has high efficiency in comparison to other methods with overall accuracy more than 93.53% and a kappa coefficient of 0.851.