Detection of petroleum hydrocarbons in continental areas using airborne hyperspectral thermal infrared data (SEBASS)

Scafutto, Rebecca Del’Papa Moreira; Lievens, Caroline; Hecker, C.A.; Meer, F.D. van der; Filho, Carlos Roberto de Souza

doi:10.1016/j.rse.2021.112323

Cited by 6 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the integration of the spectral angle cosine and spectral correlation coefficient algorithms, the classification of soil spectra was realized. Scafutto et al (2021) took advantage of airborne hyperspectral thermal infrared data to detect petroleum hydrocarbons in continental areas. Zhao et al (2012) applied both principal component analysis and a self-organizing mapping neural network-Fuzzy C-means clustering (SOM-FCM) to filter out disputes and the reduction of the dimension of coal NIR spectral data.…”

Section: Introductionmentioning

confidence: 99%

CNN coal and rock recognition method based on hyperspectral data

Yang

Chang

Zhang

et al. 2022

Int J Coal Sci Technol

View full text Add to dashboard Cite

Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing, this article proposed a convolution neural network (CNN) coal and rock identification method based on hyperspectral data. First, coal and rock spectrum data were collected by a near-infrared spectrometer, and then four methods were used to filter 120 sets of collected data: first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing. The coal and rock reflectance spectrum data were pre-processed to enhance the intensity of spectral reflectance and absorption characteristics, as well as effectively remove the spectral curve noise generated by instrument performance and environmental factors. A CNN model was constructed, and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations (i.e., the learning rate, the number of feature extraction layers, and the dropout rate) to generate the best CNN classifier for the hyperspectral data for rock recognition. The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%. Verification of the advantages and effectiveness of the method were proposed in this article.

show abstract

Section: Introductionmentioning

confidence: 99%

CNN coal and rock recognition method based on hyperspectral data

Yang

Chang

Zhang

et al. 2022

Int J Coal Sci Technol

View full text Add to dashboard Cite

show abstract

“…Scafutto, RDM, etc. used aerial hyperspectral thermal infrared data to detect petroleum hydrocarbons in the mainland [17].Zhao Kai [18] used principal component analysis, self-organizing map neural network-fuzzy C-means clustering (SOM-FCM) two-layer clustering method to effectively optimize coal samples, reduce the dimension of coal near-infrared spectroscopy data, and build The coal ash prediction model based on GA-BP neural network effectively improves the accuracy of model learning.Lei Meng [19] studied a variety of learning algorithms to improve the quality of modeling spectral data and the performance of prediction models for near-infrared spectroscopy analysis of coal ash based on machine learning. With the improvement of the requirements for intelligent tunneling technology and the development of big data technology, the number of samples has exploded, which puts forward higher requirements for coal and rock identification of hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

Research on CNN Coal and Rock Recognition Method Based on Hyperspectral Data

Yang

Chang

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing links, this article proposes a CNN coal and rock identification method based on hyperspectral data. First, collect coal and rock spectrum data by a near-infrared spectrometer, and then use four methods such as first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing to filter the 120 sets of collected data. The coal and rock reflectance spectrum data is preprocessed to enhance the intensity of spectral reflectance and absorption characteristics, and effectively remove the spectral curve noise generated by instrument performance and environmental factors.Construct a CNN model, judge the pros and cons of the model by comparing the accuracy of the three parameter combinations, select the most appropriate learning rate, the number of feature extraction layers, and the dropout rate, and generate the best CNN classifier for hyperspectral data. Rock recognition. Experiments show that the recognition accuracy of the one-dimensional convolutional neural network model proposed in this paper reaches 94.6%, which is higher than BP (57%), SVM (72%) and DBN (86%). Verify the advantages and effectiveness of the method proposed in this article.

show abstract