High-altitude and low Earth orbit (LEO) platforms assisted free-space optical (FSO) communication systems have emerged as a promising solution to facilitate the development of future integrated space-air-ground networks, owing to their expansive coverage, high bandwidth, unlicensed spectrum, and large-capacity. However, these systems are highly susceptible to the adverse effects of atmospheric turbulence, degrading overall system performance and posing a risk of signal outages. In this paper, we propose a novel semantic communication paradigm, termed prior probability-based vector quantized autoencoder (PVQAE), to enable uninterrupted and efficient image transmission in free-space optical communication systems under turbulent conditions. Specifically, it utilizes a neural network-based encoder-decoder architecture for semantic information extraction and image reconstruction, and achieves uninterrupted transmission of semantic information in turbulent channels via vector beams shift keying without the need of any adaptive optics for beam compensation. A shared discrete codebook, designed for both the transmitter and receiver, facilitates latent feature representation and enables the quantization of semantic information for light-field shift keying transmission. Moreover, a power differential detection (PDD) algorithm is derived to reduce the complexity associated with receiver-side detection. Simulation results demonstrate the superior performance of PVQAE over benchmark schemes, such as conventional methods using JPEG for image source coding and Reed-Solomon (RS) for channel coding, in terms of structural similarity index (SSIM) and compression ratio (CR) product, and classification accuracy. These results highlight the effectiveness of the proposed framework in achieving robust and efficient transmission without interruption in turbulent channels.