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.

Despite the wide applications of full-waveform light detection and ranging (FW-LiDAR) on target detection and recognizing, topographical mapping, and ecological management, etc., the mapping between the echo waveform and the properties of the targets, even for typical three-dimensional (3D) targets, has not been established. The mechanics of the modulation of targets on the echo waveform is thus ambiguous, constraining the retrieval of target properties in FW-LiDAR. This paper derived the formula of echo waveform modulated by typical 3D targets, namely, a rectangular prism, a regular hexagonal prism, and a cone. The modulation of shape, size, position, and attitude of 3D targets on the echo waveform has been investigated extensively. The results showed that, for prisms, variations in the echo waveforms under various factors essentially arise from changes in the inclination angles of their reflective surfaces and their positions relative to the laser spot. For cones, their echo waveforms can be approximated and analyzed using isosceles triangular micro-facets. The work in this paper is helpful in probing the modulation of 3D targets on echo waveform, as well as extracting the properties of 3D targets in FW-LiDAR domains, which are significant in areas ranging from topographical mapping to space debris monitoring.

Reflective Tomography LiDAR (RTL) imaging, an innovative LiDAR technology, offers the significant advantage of an imaging resolution independent of detection distance and receiving optical aperture, evolving from Computed Tomography (CT) principles. However, distinct from transmissive imaging, RTL requires precise alignment of multi-angle echo data around the target’s rotation center before image reconstruction. This paper presents an adaptive contour closure algorithm for automated multi-angle echo data registration in RTL. A 10.38 km remote RTL imaging experiment validates the algorithm’s efficacy, showing that it improves the quality factor of reconstructed images by over 23% and effectively suppresses interference from target/detector jitter, laser pulse transmission/reception fluctuations, and atmospheric turbulence. These results support the development of advanced space target perception capabilities and drive the transition of space-based LiDAR from “point” measurements to “volumetric” perception, marking a crucial advancement in space exploration and surveillance.

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.

In this work, we demonstrate a high-range-resolution LiDAR detection scheme based on mode noise characteristics. This approach achieves millimeter-level point-cloud imaging at low sampling rates below 100 MHz, effectively reducing the dependence of high-resolution ranging on high sampling rate digitization and broad tuning bandwidths, thus significantly lowering system costs. The proposed scheme eliminates the need for external modulation and has minimal requirements for the laser linewidth. By employing an unbalanced interferometer, the system exploits intrinsic laser frequency fluctuations, converting them into broadband and sufficiently intense mode noise suitable for ranging. Through differential processing, the two time-delayed mode noise signals undergo coherent cancellation, which yields pronounced frequency dips at specific locations within the power spectrum. Theoretical analysis reveals a direct relationship between the center frequencies of these dips and the relative time delay. By utilizing nonlinear curve fitting to extract the dip profiles, we achieve high-precision center frequency extraction, enabling direct distance retrieval in the frequency domain. Experimentally, the system achieves millimeter-level ranging performance at an exceptionally low sampling rate of only 61.5 MHz. Specifically, the ranging accuracy was measured to be 1.3 mm at a detection distance of 7 m and 3.07 mm at 20 m while successfully resolving adjacent targets separated by a small gap of 4 mm.