Abstract
Mine water bodies create enormous water pollution due to heavy use of water in different stages of mining. Detection and monitoring of such water bodies are necessary for environmental benefits. In the past, mine water bodies are classified along with non mine water bodies through manual intervention. The motivation of this work is to automate the process of detection of mine water bodies from Landsat 8 OLI/TIRS images. First, automated water extraction index (AWEI) is used to detect water bodies in adaptive manner. Most mine water bodies can be found near mining regions. Further, mine water bodies are detected through connected component and bounding box analysis using this cue. Coal Mine Index (CMI) can differentiate coal mine regions from other land classes. The proposed method uses the feature space of CMI to detect mine water bodies in an automated fashion with average precision, and recall of \(87.46\%\), and \(65.74\%\) respectively.
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Notes
- 1.
Precision, and Recall are defined as \(t_p/(t_p+f_p)\), and \(t_p\)/(\(t_p\)+\(f_n\)), where \(t_p\), \(f_p\), and \(f_n\), are true positive, false positive, and false negative, respectively. \(F_1\) Score is harmonic mean of Precision and Recall.
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Mukherjee, J., Mukherjee, J., Chakravarty, D. (2020). Automated Detection of Mine Water Bodies Using Landsat 8 OLI/TIRS in Jharia. In: Babu, R.V., Prasanna, M., Namboodiri, V.P. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2019. Communications in Computer and Information Science, vol 1249. Springer, Singapore. https://doi.org/10.1007/978-981-15-8697-2_45
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