Using MOD09 Data to Produce a Natural-color Image from the Blue-lackedMultispectral Remote Sensing Data

摘要

Because of atmospheric effects, some satellite sensors cover only two visual spectral bands (green and red bands) in addition to bands in the near-infrared to thermal-infrared regions, and lack a blue band. As a result, a natural-color image cannot be obtained, as the blue band is necessary in combining red, green, and blue to produce natural color. This greatly affects the application of remote sensing in many areas such as virtual reality, terrain simulation, and visual interpretation. In this study, the MODIS land surface product (MOD09) was used as reference imagery from which to select pixel samples, and a non-linear regression analysis model—a back-propagation artificial neural network (BPN)— was used to fit the spectral reflectance relationship among the blue band and red, green, and near-infrared bands. Landsat TM/MSS, ZY1-02C and SPOT blue bands were then simulated with the trained fitting model, and a natural-color image was output. The experiment result shows that the MOD09 samples trained BPN model well simulated the blue band of a multispectral image and even more informative blue band, more importantly; it can eliminate the influence of the atmospheric for the blue band to some degree. With the simulated blue band, a more realistic and informative natural-color image was acquired.