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To reduce the size of spectral data, compressive sensing imaging systems are developed to sample fewer measurements than the Nyquist‐rate ones, from which the original data can be recovered by the optimisation model and algorithm. However, this is not a cheap option for the case where the real‐time acquisition of spectral information is required. To solve this problem, the authors propose a novel sensing approach for spectral features by combining the sampling, recovery and feature extraction. Inspired by the spectral feature representation, the sampling (sensing) matrix is designed from the training spectral samples to sense the spectral features of the imaging scene, which can be utilised for classification and recognition directly. Besides, the physical realisation of the sensing matrix for compressive spectral imaging systems is demonstrated by designing new modulation patterns of the digital micro‐mirror device. The experimental results on real spectral data show the feasibility of the proposed scheme and the robustness to the quantisation error and the measurement noise. Moreover, the proposed sensing approach can reduce the cost of computation and time greatly by removing the sparse recovery and feature extraction.. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) theory enables single‐pixel compressive imaging (SPCI) popular in optical remote sensing imaging, since single‐pixel imager not only realises the acquisition and recovery of sparse images at the sampling rates significantly below the classical Nyquist rate, but also makes use of special detectors to improve sensitivity, dynamic range, spectral range and so on. However, its requirement of static imaging condition for the time‐sequential measurements makes it difficult to apply in remote sensing, since the imaging platform is always in motion relative to the imaging scene. In this study, the authors develop a new method for SPCI on moving platform in optical remote sensing. Instead of ignoring the motion during sampling, the proposed method first builds the compressive sampling model based on motion compensation during sampling, and then reconstructs the image by frame‐by‐frame recovery method and joint recovery method, respectively, in the framework of CS theory, where the recovery condition is also analysed. To validate the basic principle and the physical feasibility of motion compensation, the authors implement the numerical simulations and optical experiments, where different conditions are exploited sufficiently. In addition, the proposed method for SPCI can be also extended into other applications, such as 360° annular imaging, multi‐pixel compressive imaging as well as super‐resolution imaging. [ABSTRACT FROM AUTHOR]

We report a world record of lidar profiling of metallic Ca+ ions up to 300 km in the midlatitude nighttime ionosphere during geomagnetic quiet time. Ca+ measurements (∼80–300 km) were made over Beijing (40.42°N, 116.02°E) with an Optical‐Parametric‐Oscillator‐based lidar from March 2020 through June 2021. Main Ca+ layers (80–100 km) persist through all nights, and high‐density sporadic Ca+ layers (∼100–120 km) frequently occur in summer. Thermosphere‐ionosphere Ca+ (TICa+) layers (∼110–300 km) are likely formed via Ca+ uplifting from these sporadic layers. The lidar observations capture the complete evolution of TICa+ layers from onset to ending, revealing intriguing features. Concurrent ionosonde measurements show strong sporadic E layers developed before TICa+ and spread F onset. Neutral winds can partially account for observed vertical transport but enhanced electric fields are required to explain the results. Such lidar observations promise new insights into E‐ and F‐region coupling and plasma inhomogeneities. Plain Language Summary: Inhomogeneities (also called irregularities) in the ionospheric plasma distribution can significantly degrade satellite communications and negatively impact Global Positioning System‐based navigation systems that rely on the trans‐ionospheric radio‐wave propagation. Understanding the formation mechanisms of, thus predicting, ionospheric inhomogeneities is still a huge challenge in space weather research. Long‐lived meteoric metal ions (≫ $\gg $1 day above 100 km altitude) converge to form dense ion layers, causing plasma irregularities in the ionosphere. Therefore, it is imperative to observe and understand the transport and formation of metallic ion layers. However, it is technically challenging to detect specific ion species, especially tracing ion transport over large vertical ranges and time durations. Numerous instruments detect electron density but cannot distinguish ion species. Rocket‐borne mass spectrometers can distinguish ion species but provide only snapshots. Lidars provide a powerful tool to profile and trace metallic species over extended periods by exciting specific resonance fluorescence. The only metal ions detectable by ground‐based lidars are Ca+, and previous lidar measurements reached ∼180 km. Our Ca+ lidar upgraded with narrowband lasers has achieved high detection sensitivity, enabling the Ca+ profiling up to 300 km. Such lidar measurements of dynamical Ca+ layers open a new window to study plasma irregularities. Key Points: First lidar profiling of Ca+ ions from ∼80 to 300 km was made over Beijing in the nighttime ionosphere during geomagnetic quiet timeThermosphere‐ionosphere Ca+ layers (∼10 s cm−3) are likely formed by uplifting ions from sporadic Ca+ at ∼110 km & show complex structuresSpread F occurs concurrently with Thermosphere‐ionosphere Ca+ & strong sporadic E layers occur before the onset of TICa+ and spread F, suggesting E‐ and F‐region coupling [ABSTRACT FROM AUTHOR]