Multi-channel common-aperture optical systems, which excel at simultaneous multi-spectral information acquisition, are widely used for image fusion. However, complex systems for long-distance multi-band detection suffer from difficulties in assembly and adjustment and light vignetting. To resolve this, the paper proposes a modular design method that splits the optical path into independent modules: the common-aperture optical path adopts an off-axis reflective beam-shrinking structure to extend the focal length and ensure 100% light input, compared with coaxial multi-channel common-aperture systems. The relay optical path of each spectral channel uses a continuous zoom design for smooth detection–recognition switching. Based on the method, a three-channel common-aperture system is developed integrating visible light (VIS), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The modulation transfer function (MTF) and wavefront distribution of the common-aperture optical path approach the diffraction limit. After integration with the relay optical paths, the system, without global optimization, can achieve the following performance: the root mean square (RMS) across the full field of view (FOV) at different focal lengths for each channel is smaller than the detector pixel size (3.45 μm for VIS, 15 μm for SWIR/MWIR); the MTF exceeds 0.2 at the cutoff frequency. Subsequently, the results of the tolerance analysis verify the feasibility of the design for each module and the advantage of the modular layout in the assembly and adjustment of the system. Finally, the paper discusses the influence of parallel plates on the wavefront distortion of the system and proposes optimization thinking using freeform surfaces. The design results of the study validate the feasibility of the modular layout in simplifying the design and assembly of multi-channel common-aperture optical systems.

Deep learning has significantly advanced hyperspectral image (HSI) and synthetic aperture radar (SAR) joint classification, achieving promising land-cover mapping performance. However, most existing methods rely heavily on dense pixel-level annotations, which are costly and time-consuming to acquire in practical multimodal remote sensing applications. In this article, we propose a novel semi-supervised framework, called Dual-Branch Multimodal Scale-Interaction Transformer (DMSIT), for HSI-SAR joint classification with limited labeled samples. Specifically, the proposed DMSIT first employs dual-stream convolutional encoders coupled with a Cross-Modal Feature Separation (CMFS) module. The CMFS module explicitly disentangles modality-shared classification features from sample-specific private representations, thereby effectively reducing cross-modal feature interference. Subsequently, the separated shared features are transformed into token sequences and processed by a Multimodal Scale-Interaction Transformer (MSIT). By executing scale-aware attention, MSIT captures global cross-modal dependencies and adaptively integrates complementary multimodal features, compensating for the limited receptive fields of local convolutions. Furthermore, to exploit unlabeled data, we introduce a Graph-Guided Pseudo-Labeling (GGPL) strategy. By leveraging graph topological proximity rather than isolated prediction confidences, GGPL selects reliable pseudo-labels, effectively mitigating class imbalance and suppressing error accumulation. Extensive experiments on three datasets demonstrate the effectiveness and superiority of the proposed DMSIT in comparison with several state-of-the-art methods.

The surface tension of a liquid droplet can be determined by fitting its actual profiles using the Young–Laplace equation, effectively reducing the measurement of surface tension to an accurate determination of the droplet’s profiles. Spectral confocal sensors are high-precision, interference-resistant, non-contact measurement systems for droplet surface profiling, employing a light source together with a dispersive objective lens and a spectrometer to acquire depth-dependent spectral information. The accuracy and stability of surface tension measurements can be effectively enhanced by spectral confocal sensors measuring the droplet surface profile. Although existing spectral confocal sensors have significantly improved measurement range and accuracy, their angular measurement performance remains limited, and deviations may arise at droplet edges with large inclinations or pronounced surface profile variations. This study presents the optical design of a large-angle spectral confocal sensor. By theoretically analyzing the conditions for generating linear axial dispersion in the dispersive objective lens, a front-end dispersive objective lens was designed by combining positive and negative lenses. Based on a Czerny–Turner (C-T) configuration, the back-end spectrometer was designed under the astigmatism-free condition, taking into account both central and edge wavelength effects. Zemax was employed for simulation optimization and tolerance analysis of each optical module. The results show that the designed system achieves an axial dispersion of 1.5 mm over the 430–700 nm wavelength range, with a maximum allowable object angle of ±40° and a theoretical resolution of 3 μm. The proposed spectral confocal sensor maintains high measurement accuracy over a wide angular range, facilitating precise measurement of droplet surface tension at large inclination angles.