Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

Grating-type spectral imaging systems are frequently employed in scenes for high-resolution remote-sensing observations of the Earth. However, the entrance of the grating-type spectral imaging system is a slit or a pinhole. This structure relies on the push broom method, which presents a challenge in capturing spectral information of transiently changing targets. To address this issue, the IFU is used to slice the focal plane of the telescope system, thereby expanding the instantaneous field of view (IFOV) of the grating-type spectral imaging system. The aberrations introduced by the expansion of the single-slice field of view (FOV) of the IFU are corrected, and the conversion of the IFU’s FOV from arcseconds to degrees is achieved. The design of a spectral imaging system based on an image-slicer IFU for remote sensing is finally completed. The system has a wavelength range of 1400 nm to 2000 nm, and a spectral resolution of better than 3 nm. Compared with the traditional grating-type spectral imaging system, its IFOV is expanded by a factor of four. And it allows for the capture of complete spectral information of transiently changing targets through a single exposure. The simulation results demonstrate that the system has good performance at each sub-slit, thereby validating the effectiveness and advantages of the proposed system for dynamic target capture in remote sensing.

This paper presents the design of an airborne DMD-based dual-path multi-target imaging spectrometer that is capable of achieving instantaneous imaging over a two-dimensional large field of view and the simultaneous spectral analysis of thousands of targets. It also offers advantages such as high spatial resolution, high spectral resolution, high timeliness, and low platform requirements. However, its working mechanism inherently causes misalignment errors in the dual-path images that it obtains due to exposure time differences. To address this issue, we propose a dual-path exposure time difference compensation method based on a velocity vector field model, enabling dynamic and precise matching of the dual paths. For target image points that move beyond the field of view, we propose an attitude compensation method based on optimal angular velocity coordination. Monte Carlo simulation results show that the maximum root mean square error of the compensation method across the entire field of view is 0.9792 pixels in the x-direction and 0.7130 pixels in the y-direction. Experimental results demonstrate the effectiveness of the method, which meets the requirements for practical applications and provides a reliable foundation for the real-world implementation of dual-path multi-target imaging spectrometers.

Wide-field-of-view imaging using remote-sensing cameras is of great significance in the study of polar environments. However, because of the drastic change in the direction of Earth’s rotation velocity near the polar regions, image-shift analysis and image quality changes in polar images by large wide-space time-delayed integration (TDI) cameras are poorly understood. Therefore, in this study, a novel velocity projection method was used to obtain a mathematical model of the image-shift velocity field. A quantitative analysis of the simulation showed that the anisotropy of the instantaneous image-shift velocity field varied significantly from low to high latitudes, and rapidly decreased to zero at a very low instantaneous point when there was no anisotropy. After correcting for the camera travelling frequency and bias angle, the value of the modulation transfer function at the edges decreased by less than 5% at 196 levels of integration. Thus, a theoretical basis was provided for using a large wide-space TDI camera to photograph high latitudes. The findings of this study provide a theoretical reference for the current large field-of-view space cameras to obtain high-latitude target information for the edge fuzzy degradation problem.

The integrated interferometric imaging system has pointed out a highly promising direction for the development of high-resolution, small-volume, and lightweight optical imaging systems in orbit. However, the current integrated interferometric imaging system has the problem of a low signal-to-noise ratio (SNR) in orbit, which seriously restricts the practical application and development of the system. To solve this problem, a design method for a multi-waveguide merged multi-frame integration (MWMMFI) system is proposed in this paper. This method proposes a novel non-uniform sampling time axis, which achieves accurate extraction of multi-frame integration signals between interferometric arms, overcoming the limitations of traditional time delay integration (TDI) techniques that are not applicable to tilted optical waveguide arrays. Consequently, it extends the integration time and increases the accumulation of effective optical signals. Additionally, noise is suppressed through an accumulate first and then differentiate algorithm. Ultimately, this achieves an enhancement of the system’s SNR and optimizes the quality of the reconstructed images, all without increasing motion degradation. Simulation experiments indicate that compared to traditional systems, the MWMMFI design can increase the peak signal-to-noise ratio (PSNR) up to 4 times when the merging number is two, and up to 6.58 times when the merging number is three. This provides both technical insights and theoretical guidance for addressing the issue of enhancing the SNR of integrated interferometric imaging systems in orbit.