Acta Geodynamica et Geomaterialia

Title: NEURAL NETWORK CLASSIFICATION OF LITHOLOGICAL UNITS BASED ON INTEGRATED RADAR AND MULTISPECTRAL DATA
Authors: Rahmani Naer, Ranjbar H., Nezamabadi-pour H. and Carranza E.J.M.
DOI: 10.13168/AGG.2024.0022
Full Text: PDF file (3.9 MB)
Keywords: Neural Network, NPRTool, NFTool, Remote Sensing, Radar, Multispectral, Matlab, Lithological mapping
Abstract: Geological mapping of exposed geologic units of Earth surface is a common and important activity for geologists. This process is the first step of geological prospecting. Remote sensing can provide useful driven data for further studies and also it reduces the time and cost of this process. Sometimes it is possible that some lithologies have similar spectral responses while they have different surficial texture properties. Geological units of the Earth surface are more separable by including their textural properties along with their spectral behaviour in remote sensing so authors used spectral-Radar data integration with novel idea which is named Radar data resultant vector in this study. In this paper, two different neural network methods (Neural Pattern Recognition and Neural Net Fitting) were implemented in Matlab environment for lithological classification using two different input datasets, namely (1) only multispectral data and (2) integrated Radar-multispectral data. The reason was to evaluate the performance of Spectral-Radar fused data in lithological classification in comparison with the spectral data alone. The results show that integrated Radar-multispectral data results in better classification of lithological units due to the integration of surficial textural parameters and spectral responses of such surficial features. The results also showed that the Neural Pattern Recognition method (NPRTool) performed better than Neural Net Fitting (NFTool) method. The results further show that, among the three different algorithms of the Neural Net Fitting method (i.e., Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient), the Levenberg-Marquardt performed best.