In this paper, infrared polarization detection information acquisition technology is proposed, and the polarization characteristics of oil spills are modeled and studied. A set of long-wave infrared polarization detection equipment for oil spills is designed and built, and modeling research on oil spill polarization characteristics is carried out to accurately detect and identify oil spill types and for the faster processing of oil spill events. Oil spill accuracy is increased by defining the polarization maintenance method of the polarization optical system and reducing the polarization measurement error brought on by the imaging system. As a result, a higher than 3% contrast exists between the polarization degree image and the corrected infrared intensity image. Outdoor tests using oil, palm oil, crude oil, gasoline, and diesel oil spill types are carried out in a controlled environment to collect data on the polarization of various oil species. According to the findings, each oil species’ infrared polarization contrast with seawater is typically greater than its infrared intensity contrast. However, the polarization data of saltwater, diesel, and palm oil, which are difficult to identify in intensity data, show a noticeable difference, further proving the viability of utilizing polarization to discern oil spills.

Aiming at the problem that traditional design methods make it difficult to control the polarization aberration distribution of optical systems quickly and accurately, this study proposes an automatic optimization design method for polarization optical systems based on deep learning. The unsupervised training model based on ray tracing and polarized ray tracing was constructed by learning the reference lens structural feature data from the optical lens library, and the generalization ability of the deep neural network model was improved to achieve the automatic optimization design of the polarized optical system. The design results show that the optical system structure optimized by the network model is close to the reference lens in the full field of view and the full spectrum and that the optical system structure can be designed for different focal length requirements. The success rate of 1 million groups of initial structures designed is better than 96.403%, and the polarization effect of the optical system is effectively controlled. The proposed deep learning approach to optical design provides a new solution for future complex optical systems and also provides an effective way to improve the design accuracy of special optical systems such as polarization optical systems.

To meet the demand for high-precision target classification in complex scenes, a hyperspectral–polarimetric–LiDAR multimodal image fusion method tailored for few-shot scenarios is proposed. Feature-mapping functions for polarimetric and LiDAR images are constructed, and a multi-scale hierarchical optimization strategy is employed to jointly enhance low- and high-frequency components across modalities. This approach effectively addresses key challenges under limited training data, such as substantial cross-modal dimensional disparities and the difficulty of robust feature extraction and fusion. The proposed algorithm conducts bimodal image fusion on the NWPUSP spectral-polarization dataset and KAIST spectral-depth dataset. Compared with other fusion methods, it achieves average increases of 7.3% and 4.87% in information entropy, 53.18% and 30.35% in standard deviation, 48% and 108.28% in average gradient, as well as 96.25% and 101.13% in spatial frequency, respectively. Moreover, relying on the self-developed integrated hyperspectral-polarization imaging system and commercial LiDAR, we synchronously and efficiently acquire multimodal images including hyperspectral, polarization and LiDAR images of complex ground object scenes. Comparative experiments are implemented against six other mainstream fusion algorithms. The objective evaluation results show that the average improvements reach 7.19% in information entropy, 46.85% in standard deviation, 76.62% in average gradient and 79.74% in spatial frequency, which notably enhances the feature retention capability of fused images. Under few-shot conditions, the target recognition classification accuracy and Kappa coefficient of the fused image are improved by 9.8% and 11.05%, respectively, compared with those of the unimodal hyperspectral image. This effectively highlights targets under shadow occlusion and compensates for LiDAR’s response deficiencies to surface textures, achieving complementary advantages of multimodal images for ground object targets in complex scenes. This research provides a new solution for future optical multimodal remote sensing and image fusion.

In order to meet the high accuracy pixel-matching requirements of space-dimensional dual-coded spectropolarimeter, a dual-coded image pixel-matching method based on dispersion modulation is proposed. The mathematics of the dispersion power and the pixel matching is modeled. The relationship between different pixel-matching coefficients and the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) of reconstructed images is analyzed. An imaging system experiment consisting of a digital micromirror device (DMD), a micro-polarizer array detector (MPA), and a prism–grating–prism (PGP) is built to reconstruct a spectral linear polarization data cube with 50 spectral channels and linear polarization parameters. The contrast ratio of the reconstructed spectropolarimeter image was raised 68 times against the ground truth. It can be seen from the reconstruction evaluation analysis that the spectral data and polarization data can be matched effectively by optimizing the dispersion coefficient of the PGP. The system can effectively reconstruct when the noise SNR is greater than 15 dB. The PSNR and SSIM of the reconstruction images can be improved by increasing the pixel-matching spacing. The optimal choice of the dual-coded pixel-matching spacing is one super-polarized pixel. The spectral resolution and quality of the spectropolarimeter are improved using the pixel-matching method.

This paper introduced a snapshot spectral polarization imaging system, compressive space-dimension dual-coded spectropolarimeter (CSDS). CSDS uses a digital micromirror device (DMD), a micro polarizer array detector (MPA) and a prism-grating-prism (PGP) to reconstruct a spectral linear Stocks 4D data cube with 100 channels and 3 Stocks vectors in a single shot. The reconstructed spectral profile is compared with the measurement results from micro-spectrometer (Ocean Optics STS-VIS). The feasibility and fidelity are verified from the image and spectral reconstruction evaluations. It is demonstrated that the target material can be distinguished by CSDS.

A randomly distributed spatial particles medium Monte Carlo (MC) polarization transport model is established to solve the visible light contrail detection problem in the cirrus clouds background. To analyze the effect of ambient temperature on contrail polarization scattering characteristics, an analysis method that combining hydrodynamics with Lorenz-Mie scattering theory is proposed and a two-fluid three-dimensional physical simulation condensation model is set up. Our model has been verified through simulation, with an error rate of less than 1% compared to the simulation model proposed by Jessica. The simulation results of the method indicate that, polarization detection achieves a signal-to-background ratio (SBR) is 41.27 times higher than the traditional method under cirrus clouds background with thicknesses of 300m and 1500m. Additionally, the SBR of angle of polarization (AOP) image is 3.64 times higher than that of degree of linear polarization (DOLP) image. Moreover, as ambient temperature increases, the SBR of AOP image also increases while that of DOLP image decreases. The approach can offer valuable support for aircraft rescue operations and contribute to the study of aviation’s impact on global climate.