遥感影像变化检测自适应阈值分割的Kriging方法

An Adaptive Threshold Segmentation Method Based on Spatial Statistic Theory to High-resolution Remote Sensing Change Detection

提出了一种基于空间统计理论的自适应阈值分割方法,用于多时相高分辨率遥感影像变化检测研究。针对未确定类别的目标像素点,分别以局部区域内的变化类和非变化类为样本点,将目标点的实际值和估计值进行比较,以确定目标点的类别。为验证本方法的优越性和自适应性,分别与系列传统阈值分割方法进行了对比分析。实验结果表明,本方法能够有效去除伪变化信息,减少目标的错判、误判率,提高检测精度。

An adaptive threshold segmentation approach is proposed for multitemproal highresolution remote sensing change detection. The threshold segmentation approach is based on the spatial statistics theory, using kriging interpolation. Taking differenete images as input data, it is classified as changed, unchanged and uncertainty classes. In view of uncertain objective pixel, we use changed and unchanged pixels in local area as samples to estimate the objective pixel. Then we compare the actual value and target value of objective pixel to determine the types of uncertainty pixels. To test the advantages and adaptability of this method, a series of traditional threshold segmentation algorithms have been carried on comparctive analysis and multitemporal high-resolution remote sensing images are introduced as experimental data. Experimental results prove that the adaptive threshold segmentation in this paper can remove false changed information, reduce false alarm and improve detection accuracy by comparing with traditional threshold segmentation algorithms.