基于Contourlet变换和ICA的多时相遥感图像变化检测

Change detection of multi-temporal remote sensing images based on contourlet transform and ICA

为了提高多时相遥感图像变化检测的精确度和运算效率,本文提出了一种基于Contourlet变换和独立分量分析(ICA-Independent component analysis)的变化检测算法.利用Contourlet变换多尺度、多方向性和各向异性等性质,对图像数据进行多尺度分解,再对分解后的数据进行独立分量分析,利用改进的基于牛顿迭代的固定点ICA算法分离出互相独立的数据分量,然后将分离后的数据分量转变成图像分量,最终对变化图像分量经阈值分割实现变化检测.实验结果表明,与现有的基于PCA、基于ICA、基于小波变换与ICA三种变化检测算法相比,本文算法能有效地分离出变化信息,减少了计算的复杂性,得到的变化图像具有更高的精确度,且对背景有较强的稳健性.

In order to improve the accuracy and computational efficiency of change detection of multi-temporal remote sensing images,a change detection algorithm based on contourlet transform and independent component analysis（ICA）is proposed.Firstly,multi-scale decomposition of image data isperformed by using contourlet transform with multi-scale,directionality and anisotropy.Then independent component analysis is carried out for the decomposed data.And the independent data components are separated by the improved fixed point ICA algorithm based on Newton iteration.Next the separated data components are transformed into image components.Finally,change detection is achieved by threshold segmentation and filtering for change image components. The experimental results show that compared with the existing three change detection algorithms such as the algorithm based on PCA,the algorithm based on ICA and the algorithm based on wavelet transform and ICA,the proposed algorithm in this paper can more effectively separate change information and reduce computational complexity.The obtained change image has higher accuracy and good robustness to the background.