High-risk area monitoring and early warning model for mountain tourism based on hyper-spectral

The carrier of mountain tourism is the natural environment, and the high-risk areas in the natural environment are the main hidden dangers causing safety problems, while the existing monitoring technology has the problems of low resolution, low accuracy and high false alarm rate. In this paper, a high-risk area monitoring and early warning model for mountain tourism based on hyper-spectral image(HSI) is proposed, which is called HMW, it collects Hyper-spectral images of high-risk areas through a hyper-spectral equipment and analyzes the details of high-spectral images on the Hadoop big data platform, then compose a comprehensive threshold function CEW() from multiple indicators, and trigger an alarm when the value of CEW()is greater than the risk threshold. For different types of high-risk areas, the coefficients in the CEW() function can also be adjusted, so that the CEW() function has versatility practicality. It can be seen from the simulation experiment results of the HMW model that the HMW model has the advantages of high accuracy, timely feedback and low false-positive rate, with a delay of less than 26 seconds, and can accurately and timely feedback the safety status of high-risk areas of mountain tourism.