Application of deep convolutional networks in airborne hyperspectral lithology identification:A case study of the northern Tamusu uranium deposit
Rock and mineral identification is a key application of hyperspectral remote sensing in geological exploration.Although traditional hyperspectral remote sensing methods perform well in mineral identification,lithology identification is still a challenge.Deep learning,particularly convolutional neural networks(CNNs),is a hot topic in artificial intelligence research which provides an important framework for image recognition.This study focuses on the lithological identification in the northern region of Tamusu uranium deposit in the western Bayingebi basin with SASI airborne hyperspectral images as a data source.The paper introduced deep CNNs method in the lithology identification with airborne hyperspectral remote sensing and evaluated their application effectiveness.Based on the preprocessed SASI airborne hyperspectral images and referencing the geological map and field surveys of the test area,eight types of samples were collected:Indosinian granite,Late Variscan granite,Late Variscan granodiorite,Middle Variscan quartz diorite,Carboniferous clastic rocks,Middle-Lower Jurassic volcanic tuff,Quaternary sediments,and sericite-altered rocks.Three model structures were developed:one-dimensional CNN based on spectral features,one plus two dimensional CNN based on combined graph-spectral features,and three-dimensional CNN.These models were trained,tested,and applied to classify lithologies in the test area.The testing results of the models indicated that the overall accuracies of the one-dimensional CNN,the one plus two dimensional CNN and the three-dimensional CNN were 82.13%,86.46%and 90.90%,respectively.The evaluation and analysis of the lithology classification recognition results from the three CNN models showed that the three-dimensional CNN's results were the closest to the actual reference,providing the best differentiation and recognition performance for various lithologies in the test area.The one plus two dimensional CNN ranked second in performance,suggesting that incorporating spatial information from hyperspectral images and jointly mining graph-spectral features with CNNs can enhance image recognition accuracy and practical application results.Meanwhile,the performance of the one-dimensional CNN and the one plus two dimensional CNN was affected by the striping effect observed after splicing airborne hyperspectral images,which impacted their practical application.The three-dimensional CNN effectively mitigated this issue,indicating its better prospects for processing large-area aerial imagery.
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