Geosynchronous synthetic aperture radar (GEO SAR) plays a crucial role in various fields, such as crop growth monitoring, irrigation management, terrain and soil analysis, and natural disaster warning. However, GEO SAR images often suffer from interference caused by moving targets, which affects the accurate interpretation of agricultural scenes. This interference mainly manifests in two aspects: first, the moving targets deviate from their true positions and cause severe defocusing in GEO SAR images; second, the defocusing from the shifted moving targets can obscure the actual agricultural scenes. To address these issues, we analyze the shift and secondary phase errors caused by moving targets based on the nonstraight slant imaging geometry of GEO SAR and propose a subaperture-based enhanced backprojection (E-BP) imaging algorithm. This algorithm effectively eliminates the interference from moving targets during imaging, restores the obscured background targets, and improves image quality. To solve the problem of separating moving targets, we introduce a semantic segmentation algorithm from deep learning into the BP imaging algorithm, achieving pixel-level segmentation and removal of moving targets in subaperture images. Furthermore, to restore the content of the obscured background images, we perform amplitude complementation using the background information in the subaperture images. By employing coherent fusion technology, we restore the complete full-aperture complex data, successfully generating high-quality background scene images without moving target interference. Finally, we validate the proposed E-BP imaging algorithm using simulated and real data. The results show that the proposed algorithm has significant advantages in improving image quality and accuracy, demonstrating its potential in GEO SAR practical applications.