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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. [ABSTRACT FROM AUTHOR]

This letter investigates the polarimetric decomposition for monitoring the crop growth status over agricultural fields. Based on an existing polarimetric decomposition, the vegetation volume scattering component is removed from the full polarimetric synthetic aperture radar (SAR). Then, the estimated crop orientation is combined with the dominant scattering mechanism in the remaining ground coherency matrix to define the vegetation growth indicators for the crop growth monitoring from SAR. The proposed method is evaluated on the time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the extensive ground truth measurements collected in the framework of the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012. The results show that the scattering characteristics vary with the crop types and the phenological development stage. The random orientation is the most important case during the crop development period considered in this letter. For canola, corn, and wheat, the dihedral scattering is significant in the remaining ground coherency matrix at the early growth stage, and then, the surface scattering becomes important in the further growth. For soybean and pasture, the surface scattering dominates the remaining ground coherency matrix during the considered crop development stage. The vegetation growth indicators derived from UAVSAR data are well correlated with the ground measurements of crop height and biomass. This letter demonstrates, for the first time, the crop growth monitoring by using polarimetric decomposition via the vegetation orientation and scattering mechanisms. [ABSTRACT FROM PUBLISHER]

An improvement on the complete three-component model-based decomposition (C3MD) of polarimetric synthetic aperture radar (SAR) data is proposed in this letter. When analyzing the scattering mechanism of the third component in the original C3MD, an orientation angle rotation (OAR) is used. However, the deorientation is found more suitable to be applied to analyze the scattering mechanism of the third component, because the deorientation minimizes the cross-polarization power of a coherency matrix so as to make the coherency matrix much closer to that of surface scattering or double-bounce scattering. With applying the deorientation instead of the OAR used in the original C3MD, an improved C3MD algorithm is proposed. Experiments with E-SAR data are presented to illustrate the effectiveness of the improvement. [ABSTRACT FROM PUBLISHER]

This letter concentrates on scattering mechanism classification of polarimetric synthetic aperture radar (PolSAR) images. Scattering mechanism classes are defined as the combinations of dominant and secondary scattering mechanisms. With three metrics extracted from the observed coherency matrix, an unsupervised classifier is proposed to classify PolSAR pixels into eight combinations of surface scattering, double-bounce scattering, and volume scattering. When applying the proposed method to simulated data, the Kappa coefficient is 0.891. It effectively classifies the dominant mechanism, and the Kappa coefficient is 0.127 higher than that of the H/ $\alpha$ method. Experiment using uninhabited aerial vehicle SAR data shows that the proposed method is able to identify secondary mechanism in forests and urban areas. This method is not only a good classifier free of specific polarimetric decomposition but also can serve as a preclassification step of sophisticated classification scheme. [ABSTRACT FROM PUBLISHER]