Airborne optical imaging can flexibly obtain the intuitive information of the observed scene from the air, which plays an important role of modern optical remote sensing technology. Higher resolution, longer imaging distance, and broader coverage are the unwavering pursuits in this research field. Nevertheless, the imaging environment during aerial flights brings about multi-source dynamic interferences such as temperature, air pressure, and complex movements, which forms a serious contradiction with the requirements of precision and relative staticity in optical imaging. As the birthplace of Chinese optical industry, the Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) has conducted the research on airborne optical imaging for decades, resulting in rich innovative achievements, completed research conditions, and exploring a feasible development path. This article provides an overview of the innovative work of CIOMP in the field of airborne optical imaging, sorts out the milestone nodes, and predicts the future development direction of this discipline, with the aim of providing inspiration for related research.

Aerial infrared point target detection under nonstationary background clutter is a crucial yet challenging issue in the field of remote sensing. This paper presents a novel omnidirectional multiscale morphological method for aerial point target detection based on a dual-band model. Considering that the clutter noise conforms to the Gaussian distribution, the single-band detection model under the Neyman-Pearson (NP) criterion is established first, and then the optimal fused probability of detection under the dual-band model is deduced according to the And fusion rule. Next, the omnidirectional multiscale morphological Top-hat algorithm is proposed to extract all the possible targets distributing in every direction, and the local difference criterion is employed to eliminate the residual background edges further. The dynamic threshold-to-noise ratio (TNR) is adjusted to obtain the optimal probability of detection under the constant false alarm rate (CFAR) criterion. Finally, the dim point target is extracted after dual-band data correlation. The experimental result demonstrates that the proposed method achieves a high probability of detection and performs well with respect to suppressing complex background when compared with common algorithms. In addition, it also has the advantage of low complexity and easy implementation in real-time systems.

The image resolution is the most important performance parameter for an aerial optoelectronic sensor. Existing thermal control methods cannot eliminate the sensor’s temperature gradient, making the image resolution difficult to further improve. This article analyzes the different impacts of temperature changes on the imaging resolution and proposes modifications. Firstly, the sensor was subjected to thermo-optical simulation by means of finite element analysis, and the different impacts of temperature changes on the imaging quality were analyzed. According to the simulation results, an active thermal control method is suggested to enhance the temperature uniformity of the sensor. Considering the impacts of active and passive thermal control measures, thermal optical analysis was carried out to predict the performance of the sensor. The results of the analysis show that the imaging quality of the sensor has been significantly improved. The experimental results show that the image resolution of the optoelectronic sensor improved from 47 to 59 lp/mm, which demonstrates that the sensor can produce a high image quality in a low-temperature environment.

High-performance control of inertial stabilization imaging sensors (ISISs) is always challenging because of the complex nonlinearities induced by friction, mass imbalance, and external disturbances. To overcome this problem, a terminal sliding mode controller (TSMC) based on a novel exponential reaching law (NERL) method with a high-order terminal sliding mode observer (HOTSMO) is suggested. First, the TSMC based on NERL is adopted to improve system performance. The NERL incorporates the power term and switching gain term of the system state variables into the conventional exponential reaching law, and the convergent speed of the TSMC is accelerated. Then, an HOTSMO is designed, which considers the speed and lumped disturbances of the system as the observation object. The estimated disturbance is then provided as a compensation for the controller, which enhances the disturbance rejection ability of the system. Comparative simulation and experimental results show that the proposed method achieves the best tracking performance and the strongest robustness than PID and the traditional TSMC methods.

In Fourier transform spectroscopy (FTS), the conventional Mertz method is commonly used to correct phase errors of recovered spectra, but it performs poorly in correcting nonlinear phase errors. This paper proposes a phase correlation method–all-pass filter (PCM-APF) model to correct phase errors. In this model, the proposed improved phase correlation method can correct linear phase errors, and all-pass filters are applied to correct the residual nonlinear phase errors. The optimization algorithm for the digital all-pass filters employs an improved algorithm which combines the subtraction-average-based optimizer (SABO) and the golden sine algorithm (Gold-SA). The proposed PCM-APF model demonstrates high correction precision, and the optimization algorithm for the filters converges faster than traditional intelligent optimization algorithms.

Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured by a single-pixel detector as total intensity. To reduce the imaging time and capture high-quality images with a single-pixel imaging technique, orthogonal patterns have been used instead of random patterns in recent years. The most representative among them are Hadamard patterns and Fourier sinusoidal patterns. Here, we present an alternative Fourier single-pixel imaging technique that can reconstruct high-quality images with an intensity correlation algorithm using acquired Fourier positive–negative images. We use the Fourier matrix to generate sinusoidal and phase-shifting sinusoid-modulated structural illumination patterns, which correspond to Fourier negative imaging and positive imaging, respectively. The proposed technique can obtain two centrosymmetric images in the intermediate imaging course. A high-quality image is reconstructed by applying intensity correlation to the negative and positive images for phase compensation. We performed simulations and experiments, which obtained high-quality images, demonstrating the feasibility of the methods. The proposed technique has the potential to image under sub-sampling conditions.