Resumen
Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.