利用多尺度融合的SAR图像变化检测方法

为充分利用图像的细节信息，提高变化检测算法的鲁棒性和稳健性，本文融合了多个尺度间的特征，提出了一种自适应SAR图像变化检测方法。首先采用小波函数对对数比差异图进行多尺度分解，而后采用独立重构的方式，得到不同尺度下的重构图像。接着采用均值循环迭代分割算法，以甄别变化区域与未变化区域。最后将不同尺度下的判别结果，采用马尔科夫随机场融合的方式，来获取最终的变化二值图。通过对不同尺度下的图像进行融合，该方法不仅有效地利用了尺度信息，而且对边缘的检测更加细致。实验结果表明该算法能够有效地提高SAR图像变化检测的精度和鲁棒性。 

Employing Multi-scale Fusion for SAR Image Change Detection

A new method for change detection based on multi-scale fusion was proposed in this paper to make full use of scale information of SAR images and improve robustness and steadiness of change detection algorithms simultaneously. The proposed method started with wavelet decomposing of log-ratio difference image, and then obtained reconstructed images under different scales independently. After that, the mean-iterative threshold selection was adopted to separate the changed pixels versus the unchanged ones. At last, a fusion means based on Markov random field, was introduced to achieve the final change map that integrates results of different scales. The means taken in this paper not only effectively take full advantage of scale information but also detect more details. Experiments on two pairs of real SAR data confirm that the proposed method is able to improve accuracy and robustness of change detection effectively.