Efficient recognition of shadow information is a key prerequisite for utilizing and eliminating shadows， most of the existing studies on urban shadow detection have been attached to the multi-band synthesis of near-infrared and visible light， while the detection ability of shadows extraction from visible light still remains insufficient. In this study， based on red， green， and blue （R， G， B） high-resolution satellite images， we used color space transformation and image multi-band operation to constructed an Optimization Urban Shadow Index （OUSI） with green light band， blue light band， and luminance component. The visual effect and accuracy evaluation were also be analyzed. The results showed that a more complete urban shadow can be extracted by OUSI with an overall accuracy of 90.46%， outperforming the current common exponential method and deep learning shadow detection algorithms； the shadow detection results were the most stable as it suffered less from the influence of different land cover types. In contrast to the previous feature-based methods， the raw image data of this study only rely on RGB three-band information. The OUSI consumes fewer computing hours and thus providing an effective practical solution to achieve urban shadow detection in large areas.