利用分形和多尺度分析的中低分辨率SAR图像变化检测

Mid and Low Resolution SAR Image Change Detection Based on Fractal and Multi-scale Analysis

对于合成孔径雷达(synthetic aperture radar,SAR)图像像素级变化检测,常见的对数比、交叉熵差异图在提取建筑物等人造目标的变化时不能保持其结构特征。本文将分形维数引入到差异图构造中,定义了分形-对数比(fractal dimension-log ratio,FD-LR)融合差异图,在有效提取不同地物类型变化的同时,能够保持其轮廓结构。为克服斑噪干扰,对FD-LR进行多尺度分析,通过贝叶斯分割和决策级融合提取变化信息。实验结果表明,该方法模型简单,能够有效检测不同地物类型的变化,在中低分辨率复杂场景的SAR图像变化检测中具有优势。

For pixel based SAR image change detection, the discrepancy images produced by the log ratio operation or Kullback-Leibler divergence cannot achieve satisfactory results in artificial target change detection. We introduce a fractal dimension into the construction of discrepancy images and define the Fractal Dimension-Log Ratio （FD-LR） image capable of detecting changes both from the natural targets and the artificial targets. A Gaussian mixture distribution is used to model the statistical properties of FD-LR. The Bayesian principle with expectation maximization-based parameter estimation is conducted to perform unsupervised thresholding on the FD-LR. To reduce speckle interferences , multiscale analysis and data fusion in the decision step are performed. Comparative experiments confirm the effectiveness of the proposed approach.