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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 16; pp. 1 - 10
• Main Authors: Pan, Ting, Zuo, Renguang, Wang, Ziye
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Convolution; Convolutional neural network; Convolutional neural networks; data fusion; Data integration; Geochemical surveys; Geochemistry; Geologic mapping; Geological mapping; Geological surveys; Geology; Image enhancement; Machine learning; Mapping; Neural networks; Plant cover; Random forests; Reflectance; Remote sensing; Spatial discrimination; Spatial resolution; Spectral reflectance; Surveys; Training; Vegetation
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2023.3260584

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.