Light field cameras， with their multi-dimensional imaging capabilities and minimal resource allocation， expand the exploration boundaries of imaging applications in unstructured air-ground-sea environments. The process of light field imaging is susceptible to occlusion and noise ， and may produce unreliable depth estimation. This paper proposes a fast light fields depth estimation method for spatial occlusion-oriented， analyzes the main factors affecting the accuracy of depth estimation in depth， and establishes the optimal light field fast filtering architecture for different spatial occlusion modes. Then a highly integrated composite cost is constructed using single-bit features of pixel points to achieve depth image refinement and occlusion optimization. The experiments demonstrate that the computational efficiency of this method is significantly better than those of Markov random fields， and can reduce the MSE by 51.3%， the reliability of the depth estimation algorithm is improved at a lower operational cost， and this method is expected to provide strong support for the application of light-field imaging technology in complex scenes.