Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform
Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform
复制标题
DOI：
10.1117/12.794363
复制
发表时间：
2008-09
影响因子：
8.2
通讯作者：
T. Çelik;K. Ma
中科院分区：
文献类型：
--
作者：
T. Çelik;K. Ma
提出了一种非监督的多时相卫星图像变化检测方法。该算法利用双树复小波变换（DT-CWT）固有的多尺度结构，在每个尺度上将每个输入图像单独分解为一个低通子带和六个方向高通子带。为了避免低通子带可能发生的光照变化问题，仅分析两幅卫星图像的六个高通子带导致的DT-CWT系数差异，以确定每个子带像素强度是否发生变化。这样的二元决策是基于从混合统计模型导出的无监督阈值，目标是最小化变化检测的总错误概率。因此，为每个子带形成二进制变化检测掩码，并且通过使用尺度内融合和尺度间融合两者来合并所有产生的子带掩码，以产生最终的变化检测掩码。为了进行变化检测的性能评估，建议DT-CWT为基础的无监督变化检测方法被利用的无噪声和噪声图像。大量的仿真结果清楚地表明，该算法不仅始终提供更准确的检测小的变化，但也表现出有吸引力的鲁棒性，对各种噪声类型和噪声水平下的噪声干扰。
In this paper, an unsupervised change-detection method for multitemporal satellite images is proposed. The algorithm exploits the inherent multiscale structure of the dual-tree complex wavelet transform (DT-CWT) to individually decompose each input image into one low-pass subband and six directional high-pass subbands at each scale. To avoid illumination variation issue possibly incurred in the low-pass subband, only the DT-CWT coefficient difference resulted from the six high-pass subbands of the two satellite images under comparison is analyzed in order to decide whether each subband pixel intensity has incurred a change. Such a binary decision is based on an unsupervised thresholding derived from a mixture statistical model, with a goal of minimizing the total error probability of change detection. The binary change-detection mask is thus formed for each subband, and all the produced subband masks are merged by using both the intrascale fusion and the interscale fusion to yield the final change-detection mask. For conducting the performance evaluation of change detection, the proposed DT-CWT-based unsupervised change-detection method is exploited for both the noise-free and the noisy images. Extensive simulation results clearly show that the proposed algorithm not only consistently provides more accurate detection of small changes but also demonstrates attractive robustness against noise interference under various noise types and noise levels.