To obtain intrinsic reflectance of the scene by hyperspectral imaging systems has been a scientific and engineering challenge. Factors such as illumination variations, atmospheric effects and viewing geometries are common artefacts which modulate the way of light reflections from the object into the sensor and that they are needed to be corrected. Some of these factors induce highly scattered and diffuse irradiance which can artificially modify the intrinsic spectral reflectance of the surface, such as that in the shadows. This research is attempted to compensate the shadows in the hyperspectral imagery. In this study, three methods known as the Diffuse Irradiance Compensation (DIC), Linear Regression (LR) and spectro-polarimetry technique (SP) have been proposed to compensate the shadow effect. These methods have various degrees of shadow compensation capabilities, and their pros and cons are elucidated within the context of their classification performances over several data sets recorded within and outside of the laboratory. The spectro-polarimetry (SP) technique has been found to be the most versatile and powerful method for shadow compensation, and it has achieved over 90% of classification accuracy for the scenes with ~30% of shadow areas.