The Canadian RADARSAT constellation mission (RCM) is represented by three synthetic aperture radar (SAR) satellites, each of which includes a compact polarimetry (CP) mode. CP is advantageous because it provides increased backscatter information relative to single and conventional dual-polarized modes and has larger swath widths relative to a quad polarization mode. CP captures single-look complex data which can be used to derive the multilook complex (MLC) coherence matrix, or, equivalently, the Stokes vector data of the backscattered field. The challenge is to develop computer vision algorithms that can be used to effectively segment the scene using this new data source. An unsupervised region-based segmentation approach has been designed and implemented that utilizes the complex Wishart distribution characteristic of the MLC CP data. The segmentation method is based on the iterative region growing with semantics algorithm originally designed for single and dual pol intensity SAR data. The algorithm has been tested using both simulated CP SAR images and a pair of available quad polarization SAR images. The results demonstrate that the CP-IRGS algorithm provides more accurate segmentation images than those using only the RH and RV channel intensity images.

中文翻译：

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CP-IRGS：多视复杂紧凑偏振 SAR 数据的基于区域的分割


加拿大 RADARSAT 星座任务 (RCM) 由三颗合成孔径雷达 (SAR) 卫星代表，每颗卫星都包含紧凑偏振测量 (CP) 模式。 CP 是有利的，因为它相对于单偏振模式和传统双偏振模式提供了增加的反向散射信息，并且相对于四偏振模式具有更大的测绘带宽度。 CP 捕获单视复数数据，可用于导出多视复数 (MLC) 相干矩阵，或者等效地，反向散射场的斯托克斯矢量数据。面临的挑战是开发可用于使用这种新数据源有效分割场景的计算机视觉算法。设计并实现了一种基于区域的无监督分割方法，该方法利用 MLC CP 数据的复杂 Wishart 分布特征。该分割方法基于最初为单极化和双极化强度SAR数据设计的语义迭代区域增长算法。该算法已使用模拟 CP SAR 图像和一对可用的四偏振 SAR 图像进行了测试。结果表明，CP-IRGS 算法比仅使用 RH 和 RV 通道强度图像的算法提供更准确的分割图像。