Hyperspectral imaging systems are widely used in precision agriculture, environmental monitoring, and mineral exploration. However, current systems often suffer from high cost, large size and weight, and considerable system complexity, which hinder their widespread deployment. To overcome these limitations and achieve a better balance between performance, cost, and portability, this work aims to develop a compact, cost-effective visible-to-near-infrared (VNIR, 400–1000 nm) hyperspectral camera based on Schwarzschild configuration and commercial off-the-shelf (COTS) components. The development followed a comprehensive methodology encompassing theoretical design, simulation, prototype assembly, and performance testing. The all-reflective optical system effectively eliminates chromatic aberration and minimizes energy loss, achieving an integration time as short as several milliseconds and a push-broom frame rate of 200 fps. The optical design leveraged optical path length theory and the unobscured Schwarzschild structure to optimize off-axis mirrors and a plane grating. Optical performance was optimized and verified using simulations, which confirmed that spot sizes at all field positions were highly concentrated and that critical distortions such as smile and keystone were controlled within several pixels. A prototype was assembled on a precision optical bench using multi-axis adjustable mounts and then integrated into a precisely machined housing, achieving a total weight less than 2 kg. Calibration verified a spectral coverage of 400–1000 nm and a resolution of 5 nm. Imaging experiments demonstrated the system’s ability to resolve subtle spectral features, successfully distinguishing different vegetations and artificial materials based on their spectral signatures—particularly the strong NIR (780–1000 nm) reflectance of vegetation versus synthetic green materials. The camera offers a high-performance, low-cost solution suitable for applications including precision agriculture, environmental monitoring, mineral exploration, and others.

A wide band range can cover more of the characteristic spectral lines of substances, and thus analyze the structure and composition of substances more accurately. In order to broaden the band range of spectral instruments, an ultra-wide-band Fourier transform infrared spectrometer is designed. The incident light of the spectrometer is constrained by a secondary imaging scheme, and switchable light sources and detectors are set to achieve an ultra-wide band coverage. A compact and highly stable double-moving mirror swing interferometer is adopted to generate optical path difference, and a controller is used to stabilize the swing of the moving mirrors. A distributed design of digital system integration and analog system integration is adopted to achieve a lightweight and low-power-consumption spectrometer. High-speed data acquisition and a transmission interface are applied to improve the real-time performance. Further, a series of experiments are performed to test the performance of the spectrometer. Finally, the experimental results show that the spectral range of the ultra-wide-band Fourier transform infrared spectrometer covers 0.770–200 μm, with an accurate wave number, a spectral resolution of 0.25 cm⁻¹, and a signal-to-noise ratio better than 50,000:1.

Compressed sensing (CS) is a promising approach to enhancing the spatial resolution of images obtained from few-pixel array sensors in three-dimensional (3D) laser imaging scenarios. However, traditional CS-based methods suffer from insufficient range resolutions and poor reconstruction quality at low CS sampling ratios. To solve the CS reconstruction problem under the time-of-flight (TOF)-based pulsed-laser imaging framework, a CS algorithm based on proximal momentum-gradient descent (PMGD) is proposed in this paper. To improve the accuracy of the range and intensity reconstructed from overlapping samples, the PMGD framework is developed by introducing an extra fidelity term based on a pulse shaping method, in which the reconstructed echo signal obtained from each sensor pixel can be refined during the iterative reconstruction process. Additionally, noise level estimation with the fast Johnson–Lindenstrauss transform is adopted, enabling the integration of a denoising neural network into PMGD to further enhance reconstruction accuracy. The simulation results obtained on real datasets demonstrate that the proposed method can yield more accurate reconstructions and significant improvements over the recently developed CS-based approaches.