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The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan. [ABSTRACT FROM AUTHOR]

Highlights • We proposed a new IAM-ELM neural network algorithm. • We proposed a rapid coal classification method. • Our method has the advantage of economy, speed and accuracy. • Our method has important practical application value. Abstract Coal classification is an indispensable task in coal mining and production. The traditional method of coal classification has the disadvantages of high cost, low speed and low accuracy. Therefore, a rapid coal classification method based on visible-infrared spectroscopy is proposed in this research. First, we collected samples of different coal types and used spectrometers to measure the spectral data of these samples. Then, we proposed an improved multilayer extreme learning machine algorithm using this algorithm to build a coal classification model. The simulation results showed that the model has good classification results. Compared with the traditional coal classification methods, this method has an unparalleled advantage in economy, speed and accuracy. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM. [ABSTRACT FROM AUTHOR]

Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then we do spectrometric tests on this batch of copper ore, and get accurate spectral data of copper ore. Based on the data obtained, we propose a new two hidden layer extreme learning machine algorithm with variable hidden layer nodes and use the regularization standard to constrain the extreme learning machine. Finally, the prediction model of copper content in copper ore is established by using the algorithm. Experiments show that this method of detecting copper ore content using spectral information is completely feasible, and the algorithm proposed in this paper can detect the copper content in copper ores faster and more accurately. [ABSTRACT FROM AUTHOR]

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry. [ABSTRACT FROM AUTHOR]

• An opencast coal mine monitoring method based on multispectral remote sensing images and field reflectance data. • The advantages of this monitoring method are high speed, high accuracy and low price. • We proposed a new tree root algorithm. • We proposed a tree root extreme learning machine neural network. Coal is currently the main energy in the world. A large amount of gangue is produced during the coal mining process, and the gangue contains a large amount of metal and sulfide, which is one of the environmental pollution sources. Therefore, how to monitor changes in coal mines in real time is an important task. With regard to this problem, we propose a new method of monitoring coal mining area. First, we collect samples of coal, gangue, and other objects from the Shenhua Baori Xile coal mine, and then measure the ground measured spectral reflectance data of these samples. For satellite data, we remove the interference information of the reflectance spectra from these images and established a relationship with the ground measured data. We propose a tree root algorithm and then combine extreme learning machine to build a classification model. Finally, the model is applied to remote sensing images, and a good extraction effect is obtained. Compared with typical monitoring methods, our method has the superiorities of high precision, low-priced, and fast speed, and it can monitor the changes in the coal mine area in real time. This is the basis for improving the environmental pollution around the coal mining area. [ABSTRACT FROM AUTHOR]