双阈值合成孔径雷达SAR(Synthetic Aperture Radar)变化检测算法具有在发现变化区域的同时还能确定地表发生后向散射变化类型的优点。针对广义高斯双阈值最小误差法D-GKIT(Dual Generalized Kittler and Illingworth Thresholding)在进行阈值选取时直方图中不同类别像素灰度级重叠严重时，分割结果容易在尖峰单侧选取出双阈值而导致无法正确分割差异图的问题，本文提出一种结合归一化最大类间方差和广义高斯最小误差法GKIT(Generalized Kittler and Illingworth Thresholding)的双阈值SAR变化检测方法。首先，提出以归一化最大类间方差值作为灰度级重叠程度的判别参数，确定阈值的选取顺序及两个候选区间；然后，利用GKIT在候选区间内进行分割，获取单侧阈值及非变化类拟合函数；最后，提出利用非变化类拟合函数更新后的直方图作为另一侧阈值选取基础进行分割，得到对应分割阈值。以宁波地区高分三号(GF-3)SAR卫星影像作为试验研究数据，结果表明：本文方法能较好地解决灰度级重叠时D-GKIT无法进行正确分割的问题，具有良好的变化检测效果和更强的鲁棒性且达到了利用研究区数据验证利用GF-3号SAR卫星影像进行变化检测研究可行性的目的。

Compared with the single threshold segment method in SAR change detection, the dual thresholds segment method can simultaneously identify the change areas and confirm the change types. Although D-GKIT shows a superior performance, a strongly overlapping gray level is observed in the histogram of the difference image, thereby inaccurately identifying double thresholds on the same side of the peak. In this paper, we apply a dual-thresholds method combined with normalized maximal between-class variance and GKIT test on GF-3 images to verify its feasibility of our method and the ability of change detection ability.First, the normalized maximal between-class variance values of two sides surrounding the peak in the histogram are taken as the degrees of the overlapping gray level, and then the thresholds selection sequence and the candidate intervals are confirmed. Second, the side at which the gray level lightly overlaps is segmented by GKIT, and the threshold and the fitting function of the unchanged class are obtained. Third, the fitting function of the unchanged class is used to replace the corresponding part in the origin histogram to form a new histogram that is subsequently segmented to obtain the threshold in the second candidate interval. Finally, the two thresholds are applied on the difference image to obtain the final change result. The experiment on GF-3 SAR images reveals that the performance on our proposed method outperforms D-GKIT and can deal well with the overlapping gray level overlapped in the histogram of the difference image. The confusion matrix of the results for various local areas in the change image also shows that the proposed method has been slightly influenced by the overlapping gray level overlapped and obtains generally good results. Therefore, the feasibility of our method and its change detection ability by using GF-3 images are verified. We propose a method based on the normalized maximal between-class variance and GKIT to segment a difference image by applying dual thresholds in SAR change detection. The effectiveness of the proposed method and its change detection ability by using GF-3 images are validated by the experiment results.