高分辨率SAR影像形态学层级分析的建筑物检测

目的 现有基于结构分析的高分辨率SAR影像建筑物检测方法,只考虑了直线和L形结构建筑物,并且依赖建筑物高亮线条处阴影区作为建筑物识别的主要特征;当处于复杂场景时,阴影区受制于背景较暗或建筑物密集而无法准确得到,导致建筑物检测误差大、检测率低。针对上述问题,提出一种基于形态学层级分析的高分辨率SAR影像无监督建筑物检测算法。方法 该方法基于单幅单极化高分辨率SAR影像,首先利用改进的形态学交替滤波算子有效抑制其固有的斑点噪声,大大剔除了同质区背景噪声的干扰;然后利用层级分析形态学差分属性断面算法来实现对SAR影像建筑物的几何结构特征的提取;最后结合特征融合和属性阈值分割等后处理步骤得到复杂场景下建筑物提取信息。结果 将上述方法在建筑物密集的城区SAR影像中实验,通过与其他方法对比分析,具有检测率高、误差小的特点,准确率和召回率分别为95.38%、86.31%,并对降低虚警率方面有明显的优势。结论 将形态学交替滤波与形态学属性滤波的改进与结合,在对不同走向、尺寸和形状的高密度建筑物检测中具有较好的适应性。

Building detection of high-resolution SAR image based on morphological hierarchy analysis

Objective Existing structure-analysis-based algorithms of building detection in high-resolution synthetic aperture radar (SAR) images only consider straight-line and L-shaped buildings and utilize the shadowed areas of the highlighted lines on buildings in the detection process. In complicated scenes, shadowed areas cannot be accurately detected by restricting dark backgrounds and dense buildings;this condition results in large errors and low accuracy in building detection. Focusing on these problems, an algorithm based on the morphological hierarchical analysis of unsupervised building detection in high-resolution SAR images is developed in this study. Method The method is applied to single-polarization high-resolution SAR images. First, an improved alternating sequential filter (ASF) (i.e., extended ASF) is utilized to reduce the inherent speckle noise and eliminate the interference of background noise in the homogeneous regions significantly. Second, the differential morphological attribute profiles are calculated to implement the geometric structure feature extraction of buildings in a SAR image in a complex scene. Finally, post-processing methods, such as feature fusion and threshold segmentation, are performed to obtain intricate building information. Result Compared with other structure-analysis-based algorithms, the proposed method exhibits a higher detection rate and a smaller error for an urban area with high-density buildings. The precision and recall rates of the proposed method are 95.38% and 86.31%, respectively. The method also results in reduced false-alarm rates. Conclusion The improvement and combination of ASFs and morphological attribute profiles are suitable for the detection of high-density buildings with different directions, sizes, and shapes.