The mid-wave infrared imager has stronger transmission ability than the visible near-infrared imager, which can effectively overcome the limitation of the low-visibility climate on the image acquisition timeliness. Moreover, it is more conducive to the detection of long-distance small target. However, few infrared imager systems track the long-distance aerial target while detecting it in real-time. Meanwhile, conventional optical structures for long-distance imaging are cumbersome and bulky. Thus, the significant challenge to solve this problem is how to design a lightweight infrared imaging system for the detection of long-distance infrared small targets over complex scenes. In this article, the basic principle and design features of a compact mid-wave infrared imager system are detailed. The optical system has an ingenious design, compact structure, low power consumption, lightweight, and low installation cost. Simultaneously, an improved features from the accelerated segment test (IFAST) target detection algorithm is proposed, which is verified by comparative experiment, showing good robustness and stability for infrared small target detection. Combining IFAST with two-dimensional turntable can realize the requirement of real-time object tracking and two filed imaging experiments were carried out. The results demonstrate the robustness and stability of the imager system in detecting and tracking infrared small targets, which can provide a reference for the design of compact mid-wave infrared instruments for similar applications.

Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.

Aiming at the problem wherein temperature inversion accuracy is unstable due to the major differences in atmospheric transmittance under various observation paths, a method for measuring radiation characteristics of an aircraft engine’s hot parts and skin using a cooled middle-wave infrared camera is proposed. Based on the analysis of the aircraft’s infrared radiation characteristics, the atmospheric transmission model of any observation path was revised, the absolute radiation correction model was established, and the temperature inversion equation was calculated. Then, we used the quasi-Newton method to calculate the skin temperature and discussed uncertainty sources. After the theoretical study, an outfield test was carried out. A middle-wave infrared camera with a wavelength of 3.7–4.8 μm was applied to the actual experimental observation of the turbofan civil aviation aircraft. The ground observation distance was 15 km, and the flying height was 3 km. When implementing temperature inversion with the method presented in this paper, the surface temperature of the aircraft engine hot parts was 381 K, the correction uncertainty was ±10 K, the surface temperature of the skin was 296 K, and the correction uncertainty was ±6 K. As the experiment showed, the method in this paper can effectively implement infrared target temperature inversion and provide a reference for the quantification of infrared data.

A significant challenge in methods for anomaly detection (AD) in hyperspectral images (HSIs) is determining how to construct an efficient representation for anomalies and background information. Considering the high-order structures of HSIs and the estimation of anomalies and background information in AD, this article proposes a kernel minimum noise fraction transformation-based background separation model (KMNF-BSM) to separate the anomalies and background information. First, spectral-domain KMNF transformation is performed on the original hyperspectral data to fully mine the high-order correlation between spectral bands. Then, a BSM that combines the outlier removal, the iteration strategy, and the Reed–Xiaoli detector (RXD) is proposed to obtain accurate anomalous and background pixel sets based on the extracted features. Finally, the anomalous and background pixel sets are used as input for anomaly detectors to improve the background suppression and anomaly detection capabilities. Experiments on several HSIs with different spatial and spectral resolutions over different scenes are performed. The results demonstrate that the KMNF-BSM-based algorithms have better target detectability and background suppressibility than other state-of-the-art algorithms.

Feature extraction, aiming to simplify and optimize data features, is a typical hyperspectral image dimensionality reduction technique. As a kernel-based method, kernel minimum noise fraction (KMNF) transformation is excellent at handling the nonlinear features within HSIs. It adopts the kernel function to ensure data linear separability by transforming the original data to a higher feature space, following which a linear analysis can be performed in this space. However, KMNF transformation has the problem of high computational complexity and low execution efficiency. It is not suitable for the processing of large-scale datasets. In terms of this problem, this paper proposes a graphics processing unit (GPU) and Nyström method-based algorithm for Fast KMNF transformation (GNKMNF). First, the Nyström method estimates the eigenvector of the entire kernel matrix in KMNF transformation by the decomposition and extrapolation of the sub-kernel matrix to reduce the computational complexity. Then, the sample size in the Nyström method is determined utilizing a proportional gradient selection strategy. Finally, GPU parallel computing is employed to further improve the execution efficiency. Experimental results show that compared with KMNF transformation, improvements of up to 1.94% and 2.04% are achieved by GNKMNF in overall classification accuracy and Kappa, respectively. Moreover, with a data size of 64 × 64 × 250, the execution efficiency of GNKMNF speeds up by about 80×. The outcome demonstrates the significant performance of GNKMNF in feature extraction and execution efficiency.

The existing hyperspectral target recognition algorithms implemented on graphics processing units (GPU) have an outstanding performance in reducing the operation time. However, for push-broom hyperspectral imagery, real-time target search not only requires high computing performance for real-time response and rapid decisions but also requires synchronizing imaging, data transmission and target recognition. In terms of this problem, this paper proposes a new realtime target search method for push-broom hyperspectral imagery. This method takes the advantage of cross-execution and concurrent execution of compute unified device architecture (CUDA) streams in graphics processing units (GPU). The execution efficiency and process of this method are analyzed using GPU architecture by NVIDIA GeForce GTX745, which provides a reference for further application of real-time target search in the fields of civilian search and rescue, dangerous substances investigation and so on.