The stratospheric wind field provides significant information on the dynamics, constituent, and energy transport in the Earth's atmosphere. The measurement of the atmospheric wind field on a global basis at these heights is still lacking because few wind imaging interferometers have been developed that can measure wind in this region. In this paper, we describe an advanced compact static wind imaging Michelson interferometer (SWIMI) developed to measure the stratospheric wind field using near-infrared airglow emissions. The instrument contains a field widened and thermal compensated interferometer with a segmented reflective mirror in one arm, which replace the moving mirror in a conventional Michelson interferometer, to provide interference phase steps. The field widened, achromatic, temperature compensated scheme has been designed and manufactured. The characterization, calibration, inversion software, and test of the instrument have been completed. The capacity of two-dimensional wind, temperature, and ozone measurement of the instrument has been verified in the lab experiment and model simulation. What we believe to be the novel principle, modeling, design, and experiment demonstrated in this paper will offer a significant reference to the static, simultaneous and real-time detection and inversion of the global wind field, temperature, and ozone.

Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a complement to spectral information, but related research only focus on the clustering for HSIs without considering polarization, and cannot effectively process large-scale hyperspectral datasets. In this paper, we propose an efficient representation-based subspace clustering framework for polarized hyperspectral images (PHSIs). Combining with spectral information and polarized information, this framework is extensible for most existing representation-based subspace clustering algorithms. In addition, with a sampling-clustering-classification strategy which firstly clusters selected in-sample data into several classes and then matches the out-of-sample data into these classes by collaborative representation-based classification, the proposed framework significantly reduces the computational complexity of clustering algorithms for PHSIs. Some experiments were carried out to demonstrate the accuracy, efficiency and potential capabilities of the algorithms under the proposed framework.

In a static wind imaging Michelson interferometer we developed, one of the Michelson mirrors is divided into four quadrants, with coatings on the quadrants that provide small phase steps from one quadrant to another, realizing the four simultaneous sampling of the interferogram. Restricted by the coating process and interferometer adjustment, the instrument visibility and phase steps of the four quadrants will deviate from the design value. In the actual passive detection of the atmospheric wind field, quasi-real-time calibration is required, and the calibration will also be affected by the instrument noise. In this paper, we propose a deep-learning-based denoising algorithm that can quickly denoise the wind interferogram with no need to adjust parameters, combined with conventional least-squares fitting cosine curves to obtain the visibility and phase steps of four quadrants from a series of interferograms with varying phase differences. The proposed algorithm framework is verified by experiment, and the measurement of visibility and phase steps of the wind field interferogram is efficiently realized. It can provide a reference for the visibility and phase steps measurement of the wind imaging interferometer and may have applications in wind imaging interferometer calibration.

The Mars Atmospheric Wind Imaging Interferometer offers several advantages, notably its high throughput, enabling the acquisition of precise and high vertical resolution data on the temperature and wind fields in the Martian atmosphere. Considering the current absence of such an Interferometer, this paper introduces a novel Mars wind field imaging interferometer. In analyzing the photochemical model of O₂ (a¹Δ_g) 1.27 μm molecular airglow radiation in the Martian atmosphere and considering the impact of instrument signal-to-noise ratio (SNR), we have chosen an optical path difference (OPD) of 8.6 cm for the interferometer. The all-solid-state polarized wind imaging interferometer is miniaturized by incorporating two arm glasses as the compensation medium in its construction, achieving the effects of field-widening and temperature compensation. Additionally, an F-P Etalon is designed to selectively filter the desired three spectral lines of O₂ dayglow, and its effect is evaluated through simulations. The accuracy of the proposed compact Mars polarized wind imaging interferometer for detecting Mars’ wind field and temperature field has been validated through rigorous theoretical derivation and comprehensive computer simulations. The interferometer boasts several advantages, including its compact and small size, static stability, minimal stray light, and absence of moving parts. It establishes the theoretical, technological, and instrumental engineering foundations for future simultaneous static measurement of Martian global atmospheric wind fields, temperature fields, and ozone concentrations from spacecraft, thereby significantly contributing to the dataset for investigating Martian atmospheric dynamics.