基于双边滤波和小目标抑制的异源遥感变化检测

Heterogeneous remote sensing image change detection based on bilateral filtering and small target suppression

针对异源高分遥感影像变化检测中面临突出"伪变化"突出问题,提出了一种基于改进双边滤波和小目标抑制的变化检测方法。在传统基于全局像素的滤波策略的基础上,设计了一种分割对象边界约束条件下的改进双边滤波器,以提高对象内部像素间的空间结构一致性;此外,为进一步弱化局部异常值所导致的"伪变化",提出了一种基于高阶神经元on-off通道的小目标抑制策略;最后,采用大津法对差分信息进行分类,从而获得最终变化检测结果。通过对多组异源高分遥感影像的实验结果表明,所提出方法能够有效减小"伪变化"所造成的检测误差,总体精度可达92.2%,误检率低于8.7%,在目视分析和定量评价中均显著优于3组对比方法。

Aiming at the prominent "pseudo-change" problem in the detection of changes in heterogeneous high-resolution remote sensing images, this paper proposes an object-level change detection method based on improved bilateral filtering and small targets suppression model. On the grounds of the traditional filtering strategy based on global pixels, this paper designs an improved bilateral filter under the boundary constraint of segmented objects to improve the spatial structure consistency between pixels in the object. Moreover, in order to further weaken the "false change" caused by local outliers, the paper proposes a small target suppression strategy based on high-order neuron on-off channel. Finally, the OTSU method is used to classify the difference information and obtain the final change detection results. The experimental results of multiple groups of heterogeneous high-resolution remote sensing images show that the proposed method can effectively reduce the detection error caused by "pseudo change", the overall accuracy can reach 92.2%, and the false detection rate is less than 8.7%. It is significantly better than the three groups of comparison methods in visual analysis and quantitative evaluation.