The design of compact hyperspectral cameras with high ground resolution and large field of view (FOV) is a challenging problem in the field of remote sensing. In this paper, the time-delayed integration (TDI) of the digital domain is applied to solve the issue of insufficient light energy brought by high spatial resolution, and a hyperspectral camera with linear variable filters suitable for digital domain TDI technology is further designed. The camera has a wavelength range of 450–950 nm, with an average spectral resolution of 10.2 nm. The paper also analyzed the effects of digital domain TDI on the signal–noise ratio (SNR) and the spectral resolution. During its working in orbits, we have obtained high-SNR images with a swath width of 150 km, and a ground sample distance (GSD) of 10 m @ 500 km. The design of the hyperspectral camera has an improved spatial resolution while reducing the cost.

Infrared dim small target detection is the critical technology in the situational awareness field currently. The detection algorithm of the infrared patch image (IPI) model combined with the total variation term is a recent research hotspot in this field, but there is an obvious staircase effect in target detection, which reduces the detection accuracy to some extent. This paper further investigates the problem of accurate detection of infrared dim small targets and a novel method based on total variation weighted low-rank constraint (TVWLR) is proposed. According to the overlapping edge information of image background structure characteristics, the weights of constraint low-rank items are adaptively determined to effectively suppress the staircase effect and enhance the details. Moreover, an optimization algorithm combined with the augmented Lagrange multiplier method is proposed to solve the established TVWLR model. Finally, the experimental results of multiple sequence images indicate that the proposed algorithm has obvious improvements in detection accuracy, including receiver operating characteristic (ROC) curve, background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG). Furthermore, the proposed method has stronger robustness under complex background conditions such as buildings and trees.

Due to the sparsity and inhomogeneity of sampling points in spatial frequency domain, the effectiveness of the rules for finding two points in the target object from the autocorrelation is limited, and the reconstructed image is blurred. Traditional segmented planar imaging usually needs to enhance image sharpness and minimize artifacts with continuous modification to the reconstruction algorithm. However, if the ideal image quality is not high, there will be less space for actual image optimization after sampling. To solve this problem, a segmented planar imager based on dense azimuthal sampling lens array is proposed in this paper. The radial fill factor of the lens array is 0.5, and the number of radial-spoke photonic integrated circuits (PIC) is twice that of the traditional system, which can effectively mitigate image artifacts and improve ideal image quality. Based on the dense azimuth sampling lens array architecture, the full-chain theoretical model is established, a discrete spectrum matrix reconstruction method is proposed to reduce the space between spatial sampling points. With this method, it can achieve the continuous sampling of all integer multiples fundamental frequency within the highest frequency range including zero frequency along the baseline direction. In addition, the number of radial-spoke PICs and the effective spatial sampling radius are further simulated. The results show that the upper limit of the peak signal-to-noise ratio (PSNR) can be improved by increasing the number of azimuthal sampling PICs, and reducing the effective spatial sampling radius can weaken the noise and enhance the definition of the actual image. The research results of system performance have certain reference significance for the design of segmented planar imagers in optimizing the number of radial-spoke PICs. The method of combining structural design and sampling is of great significance for improving the imaging quality of the system.

Block‐matching and three‐dimensional filtering (BM3D) is generally considered as a milestone for its outstanding performance in the area of image denoising. However, it still suffers from the loss of image detail due to the utilisation of hard thresholding on transform domain during the phase of the basic estimate. In the frequency domain, a large amount of image detail information is in high frequency, which tends to be mixed with noise. Since its low amplitude is below the threshold, some image detail is filtered out with the noise. To retain more details, this study proposes an improved BM3D. It adopts an adaptable threshold with the core of Gaussian function during hard thresholding, which can filter out more noise while retaining more high‐frequency information. When grouping, the normalised angular distance is taken as a measure of similarity to relieve the interference of noise further and achieve a higher peak signal‐to‐noise ratio (PSNR). The experimental results show that under the background of Gaussian noise with standard deviation of 20–60, the PSNR of denoised images (with a large amount of detail), applied with the authors’ improved algorithm, can be improved by compared with original BM3D.

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

Hyperspectral imaging is an important technology in remote sensing, which is widely used in many fields such as agriculture and forestry observation, environmental monitoring, deep space exploration, etc. The miniaturization and low cost have become an important development direction for hyperspectral imagers. Simultaneously obtaining images high spatial resolution, high spectral resolution, and high signal-to-noise ratio (SNR) has become an urgent challenge in the development of miniaturized imaging spectrometers. Based on linear variable filter (LVF) spectroscopy technology and small pixel CMOS sensors, combining with time delay and integration (TDI) algorithm in digital domain, we proposed a digital integrated LVF spectral imaging method, then a miniaturized hyperspectral imaging system which weighs only 5 kg was designed. To address the spectral aliasing problem of this new imaging method, we further proposed a calibration based spectral restoration and enhancement algorithm. The experimental results showed that the miniaturized hyperspectral imager can achieve a ground sample distance (GSD) of 10m@500km, an average spectral resolution of 5.6nm, and SNR better than 30dB.