Design and Verification of Optical System for Intelligent Remote Sensing Camera

To address the issues of traditional high-resolution spatial remote sensing cameras—complex optical systems, heavy weight, long development cycles, and high costs—this study combines the optical design parameters and product characteristics of lightweight remote sensing payloads. Based on the “physical simplification–algorithm enhancement” computational imaging paradigm, an algorithm-side enhancement technical system tailored to these lightweight payloads is constructed. This paper establishes a point-spread function (PSF) model for simplified optical systems and a dedicated imaging degradation model, verifying the compensation mechanism of computational methods against optical degradation effects. It achieves high-performance imaging through “low-precision simplified optics + high-precision algorithms,” providing theoretical support and practical implementation pathways for lightweight, low-cost, and rapid-response spaceborne remote sensing payloads. Experimental results confirm the excellent imaging performance of the camera, validating the effectiveness of the proposed optical design. Compared with the baseline Mask R-CNN (region-convolution neural networks), the AP50 and overall AP (average precision) of the AS Mask R-CNN are improved by 4.0% and 1.0%, respectively. This research offers a robust technical solution for intelligent remote sensing camera modes and serves as valuable reference and technical support for the opto-mechanical co-design of high-resolution remote sensing payloads.