With the widespread deployment of high-performance sensing systems in applications such as remote sensing monitoring, intelligent surveillance, and urban management, increasingly stringent requirements have been placed on imaging systems in terms of spatial coverage and image detail resolution. However, traditional optical imaging systems are fundamentally limited by the optical system's space-bandwidth product, which defines the trade-off between field of view and resolution. To address this limitation, wide-area high-resolution imaging systems have become a major research focus in modern optical imaging research. This paper focuses on the inherent trade-off between field of view and resolution in conventional systems and presents a systematic review of four representative imaging architectures: single-device scanning systems, multi-chip mosaic systems, multi-camera array systems, and multi-scale imaging systems. Each architecture is examined in terms of imaging principles, system configuration, technical challenges, and application suitability, along with a comparative evaluation of its respective strengths and limitations. Furthermore, by grounding the discussion in the theories of space-bandwidth product and lens scaling laws, the paper reveals the physical constraints of traditional systems and explores the future potential of multi-camera architectures in areas such as multidimensional imaging, high-speed video, large dynamic range, and multimodal sensing. These insights provide theoretical guidance and strategic direction for the development of next-generation intelligent imaging systems.