ABSTRACT

Excellent operation regardless of weather conditions and superior resolution independent of sensor light are the most attractive and desired features of synthetic aperture radar (SAR) imagery. This paper proposes an exclusive multi-scale with multiple orientation approach for multi-temporal SAR images. This approach integrates pre-processing and change detection. Pre-processing is performed on the SAR imagery through speckle reducing anisotropic diffusion and discrete wavelet transform. The processed speckle-free images are designed by Log-Gabor filter bank in terms of multi-scale with multiple orientations. The maximum magnitude of multiple orientations is concatenated to obtain feature-based scale representation. Each scale is dealt with multiple orientations and is compared by band-wise subtraction to retrieve difference image (DI) coefficient. The series of the difference coefficients from each scale are add-on together to estimate a DI. Thus, the resultant image of multi-scale orientation gives perception of detailed information with specific contour. Constrained k-means clustering algorithm is preferred to achieve change and un-change map. Performance of the proposed approach is validated on three real SAR image datasets. The effective change detection is examined by using confusion matrix parameters. Experimental results are described to show the efficacy of the proposed approach.

摘要

不受天气条件影响的出色操作和独立于传感器光的卓越分辨率是合成孔径雷达 (SAR) 图像最具吸引力和最理想的功能。本文针对多时相 SAR 图像提出了一种独特的多尺度多方向方法。这种方法集成了预处理和变化检测。通过散斑减少各向异性扩散和离散小波变换对SAR图像进行预处理。处理后的无斑点图像由Log-Gabor滤波器组按照多尺度多方向设计。连接多个方向的最大幅度以获得基于特征的尺度表示。每个尺度处理多个方向，并通过带状减法进行比较以检索差异图像（DI）系数。来自每个尺度的一系列差异系数相加在一起以估计 DI。因此，多尺度方向的结果图像给出了具有特定轮廓的详细信息的感知。受约束的k- means聚类算法优先实现变化和不变化的地图。在三个真实的 SAR 图像数据集上验证了所提出方法的性能。通过使用混淆矩阵参数来检查有效的变化检测。描述了实验结果以显示所提出方法的有效性。