Spatial Compressive Optical Remote Sensing: System Design, Reconstruction Algorithm and Performance Evaluation

Optical remote sensing payloads of high resolution and large field of view are facing the problem of focal plane splicing, big data acquisition and transmission. This paper proposes a novel regime of spatial compressive remote sensing (SCRS) based on snapshot compressive imaging (SCI) technology, which reduces the amount of acquired data via an encoded compressive imaging system while maintains a satisfactory image quality by applying deep learning based reconstruction algorithms. The proposed spatial compressive imaging scheme encodes the images of different fields of view, focuses them on the same detector, and reconstructs the images with an iterative algorithm of a denoising network and projection constraints. The influence of the variations in the compression ratio on the reconstructed image quality is studied by simulation. In addition to conventional image reconstruction evaluation metrics like peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), the target detection accuracy is proposed as an important index for SCRS system performance evaluation. By the Plug-and-Play FFDNet reconstruction network, the compression ratio of SCRS can reach up to 25, with a high average PSNR up to 32 dB and a high average SSIM up to 0.82. The limited decrease in target detection performance shows that SCRS has hopeful prospects in future remote sensing application scenarios.