There are nine kinds of surface cover types in the study area. They are residential area, road, forest land, cultivated land (in the hilly area), cultivated land (in the lowland), river, flood plain, gully, and high temperature targets. In the view of the spectral analysis,12 30 representative pixels in each kind of the surface cover type are selected from remote sensing imagery, respectively. There are 270 samples in total. KMO (Kaiser–Meyer–Olkin) and Bartlett spherical degree test13 have been done to all of the selected samples. KMO statistics is an indicator which is used to compare simple correlation and partial correlation coefficients of variables. Its value range is [0, 1]. The greater the KMO value, the more suitable it is for the factor analysis of the original variables to be put into effect. Bartlett is the indicator that is used to test the difference between the actual correlation matrix and the unit correlation matrix. When the value of the significance test of Bartlett is less than a given reliability, the correlation between the original variables is significant. Thus, the original variables are suitable for factor analysis. The results of these tests show that the KMO value is greater than 0.6 and the Bartlett value is less than 0.05, which satisfy the premise of factor analysis. In R-mode factor analysis of the study area, representative samples, eigenvalues, and information quantity are in Table 1 and the factor loading matrix in Table 2 is achieved. As the accumulated information quantity of first three factors has reached 98.141% and satisfies with the requirements of little information loss, this article will focus on the first three factors with larger amounts of information and give detailed explanations of the analysis of them.