Desertification in western Rajasthan (India): an assessment using remote sensing derived rain-use efficiency and residual trend methods

Natural Hazards - Owing to its impact on global ecosystem, climate change and related effects is being perceived as a serious issue worldwide especially in the arid and semi-arid regions. Climatic...     

Post-emplacement kinematics and exhumation history of the Almora klippe of the Kumaun–Garhwal Himalaya,NW India: revealed by fission track thermochronology

International Journal of Earth Sciences - Tectonically transported crystalline thrust sheet over the Lesser Himalayan meta-sedimentary zone along the Main Central Thrust (MCT) is represented by...     

Hook-shaped type-3 superposed fold in granulite,Badarama complex,Rengali province,India

International Journal of Earth Sciences -     

Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naïve Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods.