Fusion of Geochemical Data and Remote Sensing Data Based on Convolutional Neural Network

Geochemical data and remote sensing data are two typical exploratory datasets widely employed in various kinds of research, such as mineral development and environmental protection. The fusion of these two types of datasets can provide richer and more accurate information for geoscience analysis. But the existing remote sensing–geochemical data fusion methods have problems; there is a big gap between the two kinds of data in resolution. And there are other limitations, such as low fusion quality, few optional fusion methods, and not suitable for practical production applications. This article proposes a novel convolutional neural network that effectively fuses high-resolution remote sensing data with low-resolution geochemical data, enhancing the quality of geochemical data. A new data augmentation method, which mixes data of different resolutions but with the same mapping relationship, is applied to the fusion process of remote sensing data and geochemical data in this article. Experiments show that this method can improve the quality of fusion results. And transfer learning is applied to the fusion of different elements. Ag and Cr are selected to verify the mobility and generalization of the model. Compared with the existing interpolation methods, the results show that the fusion method based on the model can better characterize the distribution law of geochemical data.