Mixed pixels widely exist in remotely sensed images.It not only influences the accuracy of target detection and classification

but also greatly hinders the development of quantitative remote sensing.A large number of spectral mix- ture analysis methods have been proposed

among which the Least Square（LS）method is a widely used technique in remote sensing to estimate fractions of materials（endmembers）existing in an image pixel.With its character of simple and effective in many application studies

it has some defects such as sensitivity to local noise

atmospheric effects and environmental radiation etc.Because the Root Mean Square Error（RMSE）is used as model fit in the LS method

when the magnitude of the spectrum changes significantly while the spectral shape is kept well which would be caused by atmos- phere

shadow and so on

the unmixing accuracy of LS method will be reduced remarkably.In this study

the spectral unmixing problem is considered as a nonlinear optimization question and a new spectral mixture analysis method based on the Spectral Correlation Matching（SCM）is proposed to overcome the defects of LS method.Different with LS method

the SCM method uses the correlation coefficient to describe the similarity between the objective and test spectrum.Based on the overall shape feature of the spectra instead of the absolute differences between the objective and test spectrum

the SCM method can reduce the influence of atmospheric effects

environmental radiation etc.In order to evaluate the per- formance of SCM method and compare it with LS method

a case study was carried out in the north-third-ring area in Bei- jing city using a Landsat ETM + and IKNOS image.The ETM + images was resampled to 28m

and the IKNOS image was first classified into four land cover types corresponding to the endmembers

then the real fraction of each endmember was calculated within a 7×7 window.The results indicated that the proposed SCM method was a better alternative to least square method

with higher accuracies for each endmember estimation than LS method.It suggests that the SCM method can be applicable to solve unmixing problem in remote sensing.