The development of laser systems leads to an increasing threat to photoelectric imaging sensors. A cubic phase plate wavefront coding imaging system is proposed to reduce the risk of damage owing to intense laser radiation. Based on the wavefront coding imaging model, the diffracted spot profile and the light intensity distribution on the observation plane are simulated. An experimental device is set up to measure the laser-induced damage thresholds and investigate the morphology of laser-induced damage patterns of the conventional and the wavefront encoding imaging system. Simulations and experimental results manifest the superior laser suppression performance of the proposed method, which can help diminish the undesirable effects of laser irradiation on an imaging sensor.

Multispectral LiDAR (MSL) systems offer a significant advantage by actively capturing both spatial and spectral information. These systems offer significant promise in supporting the comprehensive analysis and precise classification of underwater targets. In this study, we build an MSL system based on an acousto-optic tunable filter (AOTF) to investigate the feasibility of underwater sphere classification. The MSL prototype features a spectral resolution of 20 nm and 13 spectral channels, covering a range from 560 to 800 nm. Laboratory-based experiments were conducted to evaluate the accuracy of range measurements and the classification performance of the system. The spectral curves of nine distinct spheres acquired by the MSL were utilized for classification using a support vector machine (SVM). The experimental results indicate that classification using multispectral data yields a higher accuracy and Kappa coefficient. Finally, the point cloud acquired from scanning experiments further validated the MSL system’s performance. This finding preliminarily validates the feasibility of multispectral LiDAR for classifying submerged spherical targets.

A high-efficiency extra-cavity Raman generator is demonstrated using a potassium yttrium tungstate (KYW) crystal for the first time, achieving switchable yellow-green laser output at 556 nm and 560 nm. The KYW Raman crystal is single-pass pumped by 532 nm green light, which is obtained by the second-harmonic generation of a 1064 nm flash-lamp-pumped Nd:YAG electro-optic (EO) Q-switched laser. When the incident pump energy at 1064 nm is 156 mJ under a repetition rate of 1 Hz, the maximum output energy reaches 51.3 mJ at 532 nm with a slope efficiency of 41.5%. By adjusting the polarization direction of the 532 nm light, two characteristic Raman shifts of the KYW crystal can be selectively excited at 765 cm⁻¹ and 905 cm⁻¹. Furthermore, the 556 nm and 560 nm visible lasers are generated with the maximum output energies of 11.6 mJ and 16.2 mJ, corresponding to the slope efficiencies of 37.6% and 48.2%, respectively. Compared with the 532 nm pulse width (18.2 ns), an obvious Raman pulse width compression effect occurred (8.2 ns).

Hyperspectral image (HSI) classification is a fundamental remote sensing task, but the high-dimensional nature of HSI data poses challenges for acquisition, storage, transmission, and processing. To mitigate these issues, researchers have developed compressive classification methods based on the coded aperture snapshot spectral imaging (CASSI) system, enabling direct classification in the compressed domain without reconstructing the full hyperspectral cube. However, existing methods often fail to fully leverage spatial-spectral correlations, suffer from limited multi-scale feature extraction, and are constrained by fixed receptive fields and degradation caused by compression. To overcome these limitations, we propose ECSA-UNet, an end-to-end framework that directly classifies compressive spectral measurements from CASSI. The model introduces a spatial-spectral decoupling module and refines the U-Net architecture with global–local feature extraction, Spatial-Spectral Attention, a Multi-Scale Enhancement Pyramid and Cross-Stage Connections, enabling dynamic receptive fields, improved long-range dependency modeling, and robust local–global feature fusion. Moreover, the CASSI coded aperture is parameterized as a trainable component, allowing for joint optimization of coding and classification within an end-to-end learning paradigm. Extensive experiments on four benchmarks show ECSA-UNet achieves state-of-the-art accuracy, and ablations confirm each module’s contribution. In addition, it maintains an effective trade-off between parameter overhead and computational efficiency, offering a viable solution for latency-sensitive hyperspectral remote sensing tasks.