With the analysis of hyperspectral images, it is possible to understand the underlying material from a single pixel. Due to this characteristics, hyperspectral imaging (HSI) is becoming a preferred method in geology, agriculture and defense fields which require the remote sensing of the environment for the purposes of classification and target detection. However, in HSI images, there are only a few photons that get reflected from areas that are under shadow. Hence, the amplitudes of the spectral signals received from shadow areas are very small, which leads to tremendous difficulties in target detection in shadowy areas. These difficulties become much more pronounceable in areas with varying elevations. In this study, we developed two methods to find the shadow regions in hyperspectral images and compared their results. The first method, line-of-sight, uses an external sensor, the Light Detection and Ranging (LiDAR), which provides elevation information. We use the LiDAR data and detect the shadows at the time of the hyperspectral data collection. Then we match the shadows to the hyperspectral image using UTM coordinates. The second method uses only the hyperspectral data and compares each pixel to a pre-determined shadow signature to arrive at a shadow/non-shadow decision. Comparison of both methods gives insight into the reliability of both methods and allows to better deal with the shadows in hyperspectral data.