Hyperspectral remote sensing image classification based on random average band selection and an ensemble kernel extreme learning machine.

Hyperspectral remote sensing technology can explore a lot of information about ground objects, and the information is not explored in multispectral technology. This study proposes a hyperspectral remote sensing image classification method. First, we preprocess the hyperspectral data to obtain the average spectral information of the pixels; the average spectral information contains spectral-spatial features. Second, the average spectral information is randomly band selected to obtain multiple different datasets. Third, based on ensemble learning and a kernel extreme learning machine, an ensemble kernel extreme learning machine is proposed. Finally, a hyperspectral remote sensing image classification model based on the ensemble kernel extreme learning machine is established. Experiments with two common hyperspectral remote sensing image datasets demonstrate the effectiveness of the proposed method.

An Ore Image Segmentation Method Based on RDU-Net Model.

The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.

Coal identification based on a deep network and reflectance spectroscopy.

The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data of various coals and preprocess the spectra. Then, a coal identification model that uses a convolutional neural network in combination with an extreme learning machine is proposed. The two-dimensional spectral features of coal are extracted through the convolutional neural network, and the extreme learning machine is used as a classifier to identify the features. To further improve the identification performance of the model, we use the whale optimization algorithm to optimize the parameters of the model. The experimental results show that the proposed method can quickly and accurately identify types of coal. It provides a low-cost, convenient, and effective method for the rapid identification of coal in the field.

Iron ore identification method using reflectance spectrometer and a deep neural network framework.

In the first selection stage of iron ore, the ore classification accuracy plays a decisive role in subsequent work. Therefore, how to identify iron ore quickly and accurately is an important task. Traditional chemical, physical and manual identification methods have the disadvantages of high costs and high time consumption. This research proposes a new iron ore identification method, that combines deep learning with visible-infrared reflectance spectroscopy to establish an iron ore classification model. We collected iron ore samples from the Anshan iron ore area and measured the spectral data with a spectrometer. Then, a deep neural network framework is proposed based on the convolution neural network and the improved extreme learning machine algorithm, and an iron ore classification model is established based on the framework. The results show that the proposed model can effectively identify the types of iron ore, and the overall accuracy reaches 98.11%.