High-resolution urban impervious surface (UIS) is essential for social and environmental analysis. However, shadows have become a major challenge to the accurate UIS mapping in high-resolution optical images, as the low reflectance usually leads to misclassification of shadows as roads or waters. To solve this problem, we proposed a shadow free multisource stack sparse autoencoder (ShdFree-MS-SSAE) for urban shadow detection and compensation. Multisource data, including optical, SAR and LiDAR were used for the occlusion information recover. First, MS-SSAE was proposed for urban land cover classification, including shadow and non-shadow area. Then, shadow area in optical data was enhanced with a linear compensation method. Finally, MS-SSAE was applied to classify the enhanced shadow area and the non-shadow area. The results demonstrated that ShdFree-MS-SSAE framework was effective for UIS mapping, with an average improvement of 10%.