A new method of mineral absorption feature extraction from vegetation covered area

Zhao, Hu; Zhang, Lifu; Zhao, Xuesheng; Yang, Hang; Yang, Keming; Xia, Zhang; Wang, Shudong; Sun, Hao

doi:10.1109/igarss.2016.7730416

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methods of classifying rocks and minerals with TASI images are currently focused on traditional methods, which can have some drawbacks. Traditional lithology classification algorithms are mainly aimed at the spectra, and they can be grouped into two aspects: based on spectral similarity and spectral characteristics [15][16][17]. To the former, the main idea of the spectral comparison is to construct a spectral similarity measure to accomplish the lithology classification.…”

Section: Introductionmentioning

confidence: 99%

Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

Liu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

Liu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…With the development of hyperspectral remote sensing, the spectral resolution and spatial resolution have become higher and higher, so lots of novel mineral identification methods have been proposed [3,4]. Traditional mineral identification methods are mainly aimed at the spectra of minerals, including methods based on spectral similarity and methods based on spectral characteristics [5][6][7]. The mineral identification methods based on spectral similarity establish standards to measure the similarity between the identified spectrum and a reference spectrum.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery

Zhao

Deng

et al. 2020

Sensors

View full text Add to dashboard Cite

Deep learning models are widely employed in hyperspectral image processing to integrate both spatial features and spectral features, but the correlations between them are rarely taken into consideration. However, in hyperspectral mineral identification, not only the spectral and spatial features of minerals need to be considered, but also the correlations between them are crucial to further promote identification accuracy. In this paper, we propose hierarchical spatial-spectral feature extraction with long short term memory (HSS-LSTM) to explore correlations between spatial features and spectral features and obtain hierarchical intrinsic features for mineral identification. In the proposed model, the fusion spatial-spectral feature is primarily extracted by stacking local spatial features obtained by a convolution neural network (CNN)-based model and spectral information together. To better exploit spatial features and spectral features, an LSTM-based model is proposed to capture correlations and obtain hierarchical features for accurate mineral identification. Specifically, the proposed model shares a uniform objective function, so that all the parameters in the network can be optimized in the meantime. Experimental results on the hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in the Nevada mining area show that HSS-LSTM achieves an overall accuracy of 94.70% and outperforms other commonly used identification methods.

show abstract

Lithological Remote Sensing Scene Classification Based on Multi-view Data

Chen,

Li,

Qin

et al. 2024

Remote Sensing Intelligent Interpretation for Geology

View full text Add to dashboard Cite

A new method of mineral absorption feature extraction from vegetation covered area

Cited by 3 publications

References 8 publications

Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery

Lithological Remote Sensing Scene Classification Based on Multi-view Data

Contact Info

Product

Resources

About