阵列SAR高分辨三维成像与点云聚类研究

Research on High-resolution 3D Imaging and Point Cloud Clustering of Array SAR

相较于传统SAR 2维成像,SAR 3维成像技术能克服叠掩与几何失真等问题,因而具有广阔的应用前景。作为一种3维成像典型体制,阵列SAR高程维分辨率通常理论上受阵列孔径的限制,远低于距离和方位维分辨率。针对这一问题,该文通过引入邻域像素间高程的一致性假设,提出一种基于加权局域像素联合稀疏的压缩感知(CS)算法。然后利用K平均(K-means)和基于密度的空间聚类(DBSCAN)等典型聚类算法实现观测场景内特定目标(如建筑物与车辆)聚类分析。最后,实测数据实验验证了该文所提算法的有效性。

Compared with traditional Two-Dimensional(2D)Synthetic Aperture Radar(SAR)imaging,Three-Dimensional(3D)SAR imaging technology can overcome problems such as overlay and geometric distortion,thus having broad development space.As a typical 3D imaging system,the elevation resolution of array SAR is generally limited by the array aperture in theory,which is much lower than the range and azimuth resolution.To address this issue,an assumption of consistency in elevation between neighboring pixels is introduced and a re-weighted locally joint sparsity based Compressed Sensing(CS)approach is proposed for the array superresolution imaging in the height dimension.Then,typical clustering methods such as K-means and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)are used to achieve clustering analysis of specific targets(such as buildings and vehicles)in the observation scene.Finally,the experimental analysis using measured data is performed to confirm the effectiveness of the proposed algorithm.