The issue of mapping geological units during an evolving process has now reached a point where the detection and classification of geological units is carried out with the help of hyperspectral sensing. In this study, using hyperspectral image of Hyperion sensor, related to Khorramabad area in Lorestan province, using Spectral Angle Mapper and Binary Encoding Classification algorithms for detecting and separating geological units. After performing the necessary preprocesses, the MNF conversion and the PPI algorithm were applied, respectively, to reduce data and extract pure pixels on the image, respectively. From the overlapping of pure pixels with geological units and ground data, the average spectrum for each member was extracted. Then, these net members were used as inputs for the above-mentioned algorithms and image classification was performed. Field surveys and ground sampling (at the points provided by the spectral angle scanners and BEC) confirm the superiority of the SAM method in separating geological units. Finally, by checking the correctness of the algorithms by calculating the error matrix, the accuracy of the classification of each method for sam (68.83) and BEC (32.60) respectively, revealed that at the end of the algorithm SAM with a total accuracy of 68.83 was introduced as the best classification algorithm.