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[Display omitted] • Data augmentation based on spectral difference and content difference. • Spectral subtraction based on maximum or minimum values. • Augmentation of all data or training data based on spectral and content differences. • Model construction based on spectral difference and content difference. • Prediction for content of new samples through ensemble averaging. Soil analysis makes for developing precision agriculture and monitoring land quality, while the models available for spectroscopy-based chemometrics are constrained by limited samples from small areas. The paper proposed sample expansion and model construction based on spectral difference and content difference, realizing data augmentation and deep learning applied to original samples with limited numbers. The spectral subtraction based on maximum or minimum values exploited the maximum or minimum values to acquire the spectral difference and content difference, which provided a new data form for model construction. Keeping enhanced samples whose spectral difference and content difference were all zero was useful for improving model performance. Augmentation of all data or training data based on maximum or minimum values-based spectral subtraction, which sorted the contents and made them the maximum or minimum values in sequence, achieved sample expansion by the spectral difference and content difference. The model utilized the random vector functional link (RVFL) network, extreme learning machine (ELM), and one-dimensional convolutional neural network (1D CNN), which could predict the content of new samples through ensemble averaging when predicting content difference. The experimental result showed the model of the spectral subtraction based on maximum or minimum values had a similar performance to that of the original samples. Augmentation of all data improved model performance by only RVFL and ELM. Augmentation of training data verified 1D CNN was better than RVFL and ELM. The paper implements a new data augmentation method and applies CNN to original samples with inadequate numbers, which lays the foundation for an improved model and applying spectral preprocessing. [ABSTRACT FROM AUTHOR]

• A remote sensing retrieval method for saline and alkaline land based on RV-MELM. • We proposed RV-TELM, SRV-MELM, and LRV-MELM. • We used random value to solve the weight and threshold. The management of saline and alkaline land requires accurate distribution data, so it is necessary to construct a high-performance inversion model to get distribution data. The paper gets data of reflectance spectroscopy and salt content based on 62 samples in Zhenlai County. Then the paper builds the inversion model and calibration model, and draws a distribution map of salt content to make governance recommendations. The paper proposes the two-hidden-layer extreme learning machine with random values (RV-TELM) and the multiple hidden layers extreme learning machine with random values (RV-MELM) by using random value. Based on whether to specify the root mean square error (RMSE) and coefficient of determination (R2) of the test set that you want to obtain in the end, RV-MELM can be divided into two categories: RV-MELM with specified root mean square error and coefficient of determination (SRV-MELM) and RV-MELM with the largest number of searches (LRV-MELM). The paper selects LRV-MELM as the inversion model, and SRV-MELM as the calibration model. The experimental results show that the overall accuracy of the final map is 1.5286. [ABSTRACT FROM AUTHOR]

• A remote sensing inversion method for saline and alkaline land based on D-TELM. • The advantages of this method are high accuracy. • The different hidden layers of D-TELM have different activation functions. In order to obtain the distribution of saline and alkaline land in Wuyuan County and take appropriate improvement measures, it is necessary to invert the distribution map accurately. This paper introduces an analysis method for saline and alkaline land, which is used to analyze the salt content (‰), organic matter content (g/kg), and PH value. First of all, thirteen saline and alkaline land samples of Wuyuan County were collected in the field. Then, the spectral data was acquired by the SVC HR-1204 portable spectrometer, and the salt content, organic matter content, and PH value data were acquired by chemical analysis. Taking different hidden layers with different activation functions as the innovation point, the TELM is improved to propose D-TELM algorithm. Finally, D-TELM was used to construct three model of the relationship between the spectral data and the salt content, organic matter content, and PH value data, and the inversion and distribution maps were performed. The experimental result shows that most of the D-TELM models are better than the TELM models. According to the principle of minimum standard deviation, the inversion model of salt content uses the dsig-sin model, the inversion model of organic matter content uses the tanh-dsig model, and the inversion model of pH value uses the dsig-sin model. [ABSTRACT FROM AUTHOR]

The average grade of iron ore is low in China, and it is significant for the effective utilization of iron ore to obtain grades. In the paper, the high-precision model was constructed based on the spectrum of hematite and the multiple hidden layers extreme learning machine with random values and dense connection, which took the output of all previous layers as the input of each hidden layer. It has achieved high precision during the detection of the total iron content for hematite, which meets the requirements for the construction and development of new green and intelligent mines. [ABSTRACT FROM AUTHOR]