Sea ice should be detected accurately to mitigate risks to marine industries, protect coastal infrastructure, and understand the dynamic interactions between sea ice and the marine environment. In this study, we developed and validated a sea ice detection algorithm based on a multilayer perceptron (MLP) for the Bohai Sea using Geostationary Ocean Color Imager-II (GOCI-II) and Sentinel-2 data. High-resolution Sentinel-2 imagery generates the sea ice truth data, which are then resampled to align with the GOCI-II grid. Rayleigh-corrected reflectance data from GOCI-II are used as input variables for the MLP algorithm. Multiscale Window Analysis enhances detection accuracy and reduces the false detections inherent in pixel-based approaches. The performance of the detection algorithm is evaluated using different window sizes (1 × 1, 3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels). The results demonstrate that increasing the window size enhances performance metrics, underscoring the importance of incorporating the surrounding reflectance variations in sea ice detection. Additionally, the developed algorithm was applied to hourly GOCI-II images for further analysis. A qualitative evaluation confirmed that sea ice was successfully detected, and the algorithm effectively identified the temporal movement of sea ice.