The intensity value on each image pixel is considered as the result of illumination and ground reflection functions. In fact, light cast on terrain objects such as buildings and trees would cause shadows and occlusion. It is hard to recover original textures in shadow area or eliminate shadows. Many detail information is hidden or lost because of such problems. Moreover, false color tone, shape distortion and failure of image matching within shadow areas also infect image recognition. However, we can still have the bright and color information to offer shadow compensation. It is necessarily to reduce or compensate the effect caused by shadow and occlusion, and get information we need. Methods for shadow detection and compensation have been studied for many years, and it's still difficult to obtain a satisfied outcome. Therefore, it's important to figure out a useful and practical method to improve compensation result. There are many models to compensate shadows, such as HSI, HSV, HCV, YIQ, etc. Our experience shows the effectiveness of the model we proposed, and reveals details covered by shadows.