Hyperspectral remote sensing has been widely used in more and more fields nowadays, such as the oil spill analysis and chlorophyll estimation in green plants. To decompose the mixed pixels people always turns to the traditional method of Least Squares Method now. But its main drawback is that it involves a large amount of matrix operations, especially regarding to the huge dimension of hyperspectral images. So it will take much time. Motivated by this, in this paper we have developed a new model of endmember abundance estimate which is referred to as Spectral Characteristic Based Abundance Estimation Model (SCBAEM). The model is based on the fitted curve in which spectral characteristic were considered. To establish the model, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) were utilized between endmember and mixed pixels. The main contributions of the paper are summarized as follows: Firstly, we build the model by calculating normalized SAM (NSAM) and normalized SID (NSID). Secondly, to test and verify the accuracy of the model, oil slick experiment is carried out. Finally, we further conduct its application in the real hyperspectral oil spill images which is from Peng-lai 19-3C platform. The results of simulation experiments and real hyperspectral image demonstrate that the proposed model could achieve the efficiency of LSM. At the same time, the time cost can be reduced greatly. So it can satisfy the real-time need.