Geological Mapping via Convolutional Neural Network Based on Remote Sensing and Geochemical Survey Data in Vegetation Coverage Areas

Abstract

Geological mapping in vegetation coverage areas is a challenging task. In this study, convolutional neural networks (CNNs) were employed for geological mapping in a vegetation coverage area based on remote sensing images and geochemical survey data. The Gram-Schmidt fusion technology was first applied to fuse Sentinel-2A and ASTER remote sensing images to enhance the spatial resolution and enrich spectral information of remote sensing data. The fused remote sensing images were then organically integrated with geochemical survey data according to the correlations between the geochemical element contents and spectral reflectance of the objects. A case study of mapping six lithologic units in Jilinbaolige, Inner Mongolia, China was implemented using a CNN model based on the fused data. The classification map obtained an overall accuracy of 83.0%, which exhibited a better performance in contrast to random forest (RF) model. The results showed that CNNs can take full advantage of the spatial features of fused data and solve the problems of the ‘salt and pepper phenomenon’ against the shallow machine learning algorithms, and the fusion of remote sensing and geochemical data can provide rich diagnostic information for geological mapping.