Spectral–polarization imaging technology plays a crucial role in remote sensing detection, enhancing target identification and tracking capabilities by capturing both spectral and polarization information reflected from object surfaces. However, the acquisition of multi-dimensional data often leads to extensive datasets that necessitate comprehensive analysis, thereby impeding the convenience and efficiency of remote sensing detection. To address this challenge, we propose a fusion algorithm based on spectral–polarization characteristics, incorporating principal component analysis (PCA) and energy weighting. This algorithm effectively consolidates multi-dimensional features within the scene into a single image, enhancing object details and enriching edge features. The robustness and universality of our proposed algorithm are demonstrated through experimentally obtained datasets and verified with publicly available datasets. Additionally, to meet the requirements of remote sensing tracking, we meticulously designed a pseudo-color mapping scheme consistent with human vision. This scheme maps polarization degree to color saturation, polarization angle to hue, and the fused image to intensity, resulting in a visual display aligned with human visual perception. We also discuss the application of this technique in processing data generated by the Channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS). Experimental results demonstrate a significant enhancement in the information entropy and average gradient of the fused image compared to the optimal image before fusion, achieving maximum increases of 88% and 94%, respectively. This provides a solid foundation for target recognition and tracking in airborne remote sensing detection.

This paper presents a modified Bidirectional Reflectance Distribution Function (BRDF) model based on the Cauchy–Lorentz distribution that accurately characterizes the reflected energy distribution of typical materials, such as metals and coatings in hemispherical space. The proposed model overcomes the problem of large errors in classical models when detecting angles far away from the specular reflection angle by dividing the reflected light into specular reflection, directional diffuse reflection, and ideal diffuse reflection components. The newly added directional diffuse reflection component is represented by the Cauchy–Lorentz distribution, and its parameters are directly obtained from experimental measurement curves without distribution fitting. Surface morphology and model parameters are determined through measurements, and the comparison between simulation and actual measurement results shows that the modified BRDF model is in excellent agreement with the measured data. The proposed model not only achieves higher accuracy and universality, but it also represents a significant advancement in the field of BRDF modeling research. Its contributions have profound implications for advancing the state of the art in BRDF modeling, as well as having a broader impact on computer graphics and computer vision domains.

This paper presents a novel approach for clustering spectral polarization data acquired from space debris using a fuzzy C-means (FCM) algorithm model based on hierarchical agglomerative clustering (HAC). The effectiveness of the proposed algorithm is verified using the Kosko subset measure formula. By extracting characteristic parameters representing spectral polarization from laboratory test data of space debris samples, a characteristic matrix for clustering is determined. The clustering algorithm’s parameters are determined through a random selection of points in the external field. The resulting algorithm is applied to pixel-level clustering processing of spectral polarization images, with the clustering results rendered in color. The experimental results on field spectral polarization images demonstrate a classification accuracy of 96.92% for six types of samples, highlighting the effectiveness of the proposed approach for space debris detection and identification. The innovation of this study lies in the combination of HAC and FCM algorithms, using the former for preliminary clustering, and providing a more stable initial state for the latter, thereby improving the effectiveness, adaptability, accuracy, and robustness of the algorithm. Overall, this work provides a promising foundation for space debris classification and other related applications.

Available polarization-difference imaging techniques face serious challenges in imaging speed and application range. To address these issues, this paper proposes an underwater dynamic polarization-difference imaging method with greater applicability. First, the intensity distribution of backscattered light is estimated via the Stokes vector. Afterward, the differential operation between the total intensity of light and the amplified estimation result of backscattered light makes clear imaging immediately accessible. Regardless of the movement states and polarization characteristics of the target, experimental results consistently demonstrate that the backscattered light can be eliminated to a great extent, and imaging quality and applicability are significantly enhanced. Meanwhile, the proposed method is immune to unexpected factors such as uneven illumination and has good stability. More importantly, there are also apparent advantages in terms of imaging time.

A novel channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS) is introduced, which is based on a double Wollaston prism (DWP). With an adjustable air gap (AG), the spectral resolution can be adjusted by changing the AG. The CSBFTIS combines the channel-modulated imaging spectropolarimeter and the slit-free static birefringent Fourier transform imaging spectrometer technology with adjustable spectral resolution. The device is compact and robust, with a wide spectral range and a large luminous flux. Compared with various previous spectropolarimeters, it can greatly reduce the size of the spectral image data to adapt to different application requirements. A prototype is built, and simulation and experiments are carried out, and the results prove the effectiveness of the method.

The optical displacement calculation formula for the dual Wollaston prism is derived for any incident plane and angle using the wave normal tracing method. The validity of the calculation is confirmed by comparing it with captured photographs. The relationship between the maximum incident aperture, incident angle, and incident surface angle is given when plane and spherical waves incident on the prism group. The aperture utilization of a novel static birefringent Fourier transform imaging spectrometer based on dual Wollaston prisms is also analyzed. The relationship of aperture utilization with the incident surface angle, incident angle, and prisms’ air gap thickness is given. The results provide a theoretical foundation for fully describing the optical transmission characteristics of the dual Wollaston prism group and developing a high-performance spatio-tempo hybrid modulated birefringent spectral zoom Fourier transform imaging spectrometer.