Polarimetric second-moment response: A target characterization benchmark and its application in ship structure recognition

Ship structure recognition using polarimetric information is of great significance for maritime remote sensing. However, accurate and reasonable ship structure recognition remains challenging, primarily due to the insufficiency of information exploration in target characterization space, and the lack of intelligent recognizer based on polarimetric-physical coupling. To address these limitations, we propose a novel Polarimetric Second-Moment Response (PSMR) and a polarimetric prior-based structure recognition framework. First, a three-dimensional polarization state vector (Q Vector) is derived, establishing a fundamental base for electromagnetic wave polarization state in complex space that simultaneously describes polarization orientation and ellipse angles and phase information. Second, for the first time, the PSMR concept is proposed, which provides a benchmark for target characterization. The PSMR spectrums are proposed to represents the polarimetric response, which reflects the polarimetric characteristics of each structure. And a designed average polarization similarity measure (APSM) quantifies the difference between the target and canonical structures by comparing their spectrums. Third, a polarimetric prior-based structure recognition framework PSAResNet is constructed, in which a polarimetric similarity attention (PSA) branch module and a similarity approximation loss function (SAL) are incorporated based on the APSM. The improved framework enhances the fusion of the neural network with polarimetric priors, enabling superior and more generalized performance of the structure recognition under measured environment. Experimental results on three measured PolSAR data of partial-coherent and distributed targets demonstrate that the proposed method achieves precise and physically consistent ship structure recognition. Furthermore, this study discusses the correlation between structure recognition and characteristics represented by model-based polarimetric decomposition. • Defining a Q Vector to establish a fundamental base for EM wave polarization state. • Proposing a PSMR spectrum to provide a target structural characterization benchmark. • Designing a APS-measure to quantify the distribution differences of PSMR spectrums. • Constructing a prior-based PRAResNet to achieve reliable ship structure recognition.