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.

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.