基于对偶树复小波变换与PCA方法结合的图像变化检测算法研究

图像变化检测是遥感图像处理领域重要方向，大多数变化检测算法都存在算法复杂度高、抗噪性弱等缺陷，利用对偶树复小波变换的平移不变性与能提高方向分辨率的优点，把对偶树复小波变换运用于变化检测中，可以提高图像细节变化的检测和算法抗噪性。首先用对偶树复小波变换对图像进行尺度分解，把图像在每个尺度上分解成一个低通子图和六个方向的高通子图。然后运用PCA(主向量分析法）提取每个尺度与方向上的特征并降维，然后运用k均值算法将图像像素分成为变化与不变化两类，最后通过多尺度融合，得到变化检测图像。

Image change detection based on dual-tree complex wavelet transform and principal component analysis

Image change detection is a very important part of remote sensing image processing.Many algorithms have defects, such as highly complex or weakly antinosie. Since the dual-tree complex wavelet transform (DT-CWT) is shift invariant and has improved directional resolution, the DT-CWT is introduced in image change detection in order to provide accurate detection of small changes and attractive robustness against noise. Firstly, the DT CWT is used to decompose the image into a low pass subband and six directional high-pass subbands at each scale. Secondly, principal component analysis (PCA) is used to create eigenvector and k means is used to categorize pixels into two parts (change and unchanged). Finally, both the intrascale fusion and the interscale fusion are used to detect the changed images.