At present, a water extraction technology is good at extracting medium- and low-resolution remote sensing images; however, when applied to high-resolution images in small water bodies, it is prone to the influence of mixed image elements, foreign body common spectrum, and shadow, resulting in misjudgment. In view of the simultaneous problems of large and small water bodies in high-resolution images, to quickly extract large water bodies and effectively avoid the impact of shadows in the extraction of small water bodies, multiscale segmentation and spectral difference segmentation are used to extract large water bodies. Consequently, a novel water extraction method based on the combination of light green ratio (LGR) and object is proposed for the impact of small water body shadows. The effectiveness of the proposed method is validated in comparison with methods such as decision tree, support vector machine, random forest, normalized difference water index + near infrared (NDWI+NIR), and convolution neural network, as a result, the accuracy for extracting water bodies is 94.86%, 88.85%, 87.15%, 88.8%, 91.46%, and 92.42% respectively, indicating its higher extraction accuracy for large and small water bodies in high-resolution images and better extraction efficiency than the comparison methods through segmentation of different scales.