Component Ratio-Based Distances for Cross-Source PolSAR Image Classification

Many polarimetric features, including decomposition components, can be extracted from polarimetric synthetic aperture radar (PolSAR) data. The polarimetric features usually reflect the physical mechanisms of ground targets and play an important role in PolSAR image classification. However, the feature values may vary largely due to the differences in system parameters of PolSAR sensors, which result in that the trained classifiers on sample data from one source PolSAR image scene may perform poorly in another source PolSAR image scene. The direct use of polarimetric features can produce wrong identifications. In this letter, we mainly deal with the components extracted by different decomposition methods and proposed a simple but efficient component ratio-based distance (CRD), which is an intracross-component distance, in contrast with component-to-component distances. The combinations with $\mathcal {L}_{1}$ distance and $\chi ^{2}$ distance can generate $\mathcal {L}_{1}$ -CRD and $\chi ^{2}$ -CRD and benefit from their robustness to small values. CRDs capture correlations between scattering components with only a linear computational complexity. Finally, we replace the distance measurement in k-nearest neighbor (KNN) with CRDs and employ the improved classifiers to classify PolSAR images. Based on the ratios of scattering components, CRD can also be used for cross-source PolSAR images, ignoring the differences in sensors, acquired time, imaging scenes, and even wavebands. Preliminary experiments on real PolSAR data sets demonstrate promising results of CRDs for image classification.