SPOT6 is a new remote sensing satellite launched in 2012, with high spatial resolution and strong data acquisition ability. However, a complete data preprocessing technology for the regulation of land resources has not yet been formed. According to the characteristics of SPOT6 satellite images, four different image fusion methods – Gram-Schmidt, HPF, PanSharpand PanSharpening were selected to conduct the comparison experiment by using the software platforms of ENVI, ERDAS and PCI. We evaluate the results’ performances from 3 different aspects. First, evaluating the image quality of experiment results qualitatively, then assessed quantitatively by establishing evaluation indexes including mean, standard deviation, information entropy, average gradient and correlation coefficient. Finally, evaluating the applicative effect of fused images based on the classification accuracy. The analysis results shows that the method of PanSharp is best to extract construction land information. Based on the PanSharp fusion image, in order to obtain the texture information under different scales, the authors screened the texture features according to Shannon entropy, and then used distance-based approach J-M to calculate the separation for choosing the optimal texture window. Once got the texture information, combining it with the original image to participate in the multi-scale image classification. The research result showed that multi-window texture participation in classification can improve separation of objects. Finally we extract construction land information with the method of SVM. This study may provide the technical support for application of SPOT6 image in the land resources management.