Traditional geological mapping, relying on field observations and rock sample analysis, is inefficient for continuous spatial mapping of features like alteration zones. By combining remote sensing data from multispectral and hyperspectral sensors with field observations, different alteration zones can be identified efficiently. Convolutional neural networks (CNNs) excel in extracting and classifying features from high-dimensional data. In our recent study, CNNs using Landsat 8, Landsat 9, and ASTER data delineate alteration zones in a mineral-rich region in New South Wales, Australia. Compared to traditional methods, CNNs slightly outperform in capturing spatial patterns, with ASTER data producing the most accurate maps for hydrothermal alteration zones.
- matplotlib
- numpy
- pandas
- pyrsgis
- sklearn
- tensorflow
@article{Farahbakhsh2024,
title = {A comparative analysis of convolutional neural networks vs. traditional machine learning models for alteration mapping with remote sensing data},
author = {Farahbakhsh, Ehsan, Goel, Dakshi, Pimparkar, Dhiraj, Chandra, Rohitash and M{\"u}ller, R. Dietmar},
year = {2024},
journal = {?},
volume = {?},
number = {?},
pages = {?},
doi = {?},
}