Based on the GF-1 satellite images

the study used five different methods including high-pass fusion

Ehlers transformation

Brovey transformation

Gram-Schmidt spectral sharpening

and principle components spectral sharpening to obtain an optimal method for image enhancement of production and construction projects. To access the performance of these fusion methods

the quality of the fused image was evaluated by the visual and statistical analyses. Moreover

the object-oriented classification method was used to examine the accuracy of extracting the disturbance information on production and construction projects in the central area. Results showed that the principle components and gram-schmidt fusion algorithm achieved better fusion of multispectral spectral information and panchromatic image spatial information

and therefore

higher spectral fidelity

richer spatial information and better visual effect were obtained. Quantitative analysis showed that the principle components transform preserved the spectral characteristics of the original multispectral image to the maximum extent. The gram-schmidt fusion algorithm had the advantage of gain

and the fused image had the largest amount of information and the most detailed texture. Overall

the principle components and Gram-Schmidt image fusion algorithms had great advantages in information extraction of disturbance information on production and construction projects. The extraction accuracy in the coal-covering area was 100%

and greater than 80% in the mixed area of bare land and construction land and the classification accuracy was 10% higher than that of the original multispectral image. This research provide a solid foundation for promoting the efficient application of domestic high-resolution remote sensing data in water and soil conservation regulation of production and construction projects

and is of great significance to advance the efficiency and informationization of soil and water conservation supervision on production and construction projects.