Lithological Classification Using Densely Connected Convolution Network on Landsat-9 and Aster Datasets in a Semi-Arid Environment

Remote sensing-based geological interpretation provides insight from numerous sensors. That notwithstanding, high-dimensional feature learning requires full-scale diagnosis of intrinsic geological features in remotely sensed datasets. This study proposes a multi-sensor fusion approach. Integrating Landsat-9 and ASTER spectral features with DenseNet for lithological classification, displaces RF and KNN by 2.12 % and surpasses ResNet50 and Xception with a classification accuracy of 83.89%. Furthermore, this study underscores the effectiveness of merging new and old generation sensors for geological interpretation.