Automated Seasonal Detection of Coal Surface Mine Regions from Landsat 8 OLI Images

Abstract

Detection, and monitoring of surface mining region have various research aspects. Coal surface mining has severe social, ecological, environmental adverse effects. In the past, semisupervised and supervised clustering techniques have been used to detect such regions. Coal has lower reflectance values in short wave infrared I (SWIR-I) than short wave infrared II (SWIR-II). The proposed method presents a novel approach to detect coal mine regions without manual intervention using this cue. Clay mineral ratio is defined as a ratio of SWIR-I to SWIR-II. Here, unsupervised K-Means clustering has been used in a hierarchical fashion over a variant of clay mineral ratio to detect opencast coal mine regions in the Jharia coal field (JCF), India. The proposed method has average precision, and recall of 76.43%, and 62.75%, respectively.