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Highly accurate supervised deep learning-based classifiers for polarimetric synthetic aperture radar (PolSAR) images require large amounts of data with manual annotations. Unfortunately, the complex echo imaging mechanism results in a high labeling cost for PolSAR images. Extracting and transferring knowledge to utilize the existing labeled data to the fullest extent is a viable approach in such circumstances. To this end, we are introducing unsupervised deep adversarial domain adaptation (ADA) into PolSAR image classification for the first time. In contrast to the standard learning paradigm, in this study, the deep learning model is trained on labeled data from a source domain and unlabeled data from a related but distinct target domain. The purpose of this is to extract domain-invariant features and generalize them to the target domain. Although the feature transferability of ADA methods can be ensured through adversarial training to align the feature distributions of source and target domains, improving feature discriminability remains a crucial issue. In this paper, we propose a novel polarimetric scattering characteristics-guided adversarial network (PSCAN) for unsupervised PolSAR image classification. Compared with classical ADA methods, we designed an auxiliary task for PSCAN based on the polarimetric scattering characteristics-guided pseudo-label construction. This approach utilizes the rich information contained in the PolSAR data itself, without the need for expensive manual annotations or complex automatic labeling mechanisms. During the training of PSCAN, the auxiliary task receives category semantic information from pseudo-labels and helps promote the discriminability of the learned domain-invariant features, thereby enabling the model to have a better target prediction function. The effectiveness of the proposed method was demonstrated using data captured with different PolSAR systems in the San Francisco and Qingdao areas. Experimental results show that the proposed method can obtain satisfactory unsupervised classification results. [ABSTRACT FROM AUTHOR]

As a special dual-polarization technique, compact polarimetric (CP) synthetic aperture radar (SAR) has already been widely studied and installed on some spaceborne systems due to its superiority to quad-polarization; moreover, quad-pol information can be explored and reconstructed from the CP SAR data. In this paper, a refined model is proposed to estimate the quad-pol information for the CP mode. This model involves CP decomposition, wherein the polarization degree is introduced as the volume scattering model parameter. Moreover, a power-weighted model for the co-polarized coherence coefficient is proposed to avoid the iterative approach in pseudo-quad-pol information reconstruction. Experiments were implemented on the simulated Gaofen-3 and ALOS-2 data collected over San Francisco. Compared with typical reconstruction models, the proposed refined model shows its superiority in estimating the quad-pol information. Furthermore, terrain classification experiments using a complex-value convolutional neural network (CV-CNN) were performed on AIRSAR Flevoland data to validate the reconstruction effectiveness for classification applications. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) polarimetric datasets are widely used in the detection and classification of urban areas. Most methods used today are based on the decomposition of fully polarimetric SAR data, which allows for the extraction of physical information about the nature of the medium and the application of proper classification methods. According to the theory, the main and predominant backscattering mechanism for buildings is double bounce. However, when analyzing urban environments, the observed predominant backscatter may differ from theory depending on many aspects. In this paper, we analyze fully polarimetric ALOS PALSAR data for various cities located on different continents, proving that the theory does not hold for most cases. There are many factors that have an impact on the detected backscatter mechanism, and the theoretical principle of predominant double bounce in urban areas can be met only under specific conditions. These factors are, among others, the orientation of the buildings, the dimensions of the streets, the type of construction (i.e., numerous planes on the roof), etc. This paper also mentions the canonical example of San Francisco, widely analyzed in the literature, as a case showing the impact of building deorientation on double bounce scattering. This area of interest is also discussed in terms of the impact of SAR data resolution on the detection of specific backscatter mechanisms. The findings of this work are very useful for increasing the awareness of the utilization of classification approaches where only pixels with double bounce backscatter mechanisms are classified as urban areas. Moreover, the article lists factors that should be taken into consideration when performing urban area detection based only on polarimetric data and standard algorithms, such as street and building orientation, building heights, and structures. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments. [ABSTRACT FROM AUTHOR]