Abstract
In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.
Highlights
Our work focused on applying typical convolutional neural networks (CNNs)
Ri is the size of the kernel along the spectral dimension, w is the value of position (p, q, r) connected to the mth feature map, and bij is the bias of the jth feature map in the ith where m indexes the feature map in the (i − 1)th layer connected to the current feature map, and Pi and Qi are the height and width of the spatial convolution kernel, respectively
The sericite phyllite classification results of support vector machine (SVM), random forest (RF), neural network (NN) and 1-D CNN are slightly better than spectral angle mapping (SAM), spectral information divergence (SID) and Fully constrained linear spectral unmixing (FCLSU)
Summary
Spectral angle mapping (SAM) [18,19] and spectral information divergence (SID) [20] are both effective in similarity measurement when the spectral vector is used for direct retrieval This type of method tends to focus on the overall waveform features of the spectrum and ignores some of the detailed features, which makes it difficult to classify similar rock types. It is usually challenging to obtain the accuracy of the mineral type of rocks, which makes adequate capture of detail in the resulting lithology classification map challenging These algorithms perform classification or recognition by extracting the shallow features of each pixel, i.e., spectral features, without considering the deeper features.
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