Study On Information Extraction Of Rural Residential Area And Population Estimation Of Coal Pressure Village Based On Remote Sensing Image

As the main energy source in China,with the continuous expansion of production demand,coal mining volume is increasing year by year,which brings a series of problems such as ground collapse,cracks in the walls of above ground buildings,solid waste discharge pollution,water and soil loss,and has a negative impact on the lives of residents in the mining area.In order to realize the efficient development of coal resources and guarantee the quality of life of residents in the mining area,it is urgent to renovate or relocate some coal villages.The statistics of the area and population of coal villages is an important prerequisite for the comprehensive management of coal villages.With the rapid development of modern satellite remote sensing technology,the resolution of remote sensing image is getting higher and higher.It is of great significance to use satellite remote sensing image for large-scale population estimation.Therefore,in order to quickly obtain the population data of Jining coal pressure village,based on the existing data and sentinel-2a remote sensing satellite image as the data source,this study uses the supervised classification method to extract the information of Jining rural residential area,calculates the rural residential area with the township as the unit,analyzes its correlation with the rural population in the same period,and constructs the population remote sensing estimation Model.Finally,the applicability of the model in Jining coal village is evaluated,and then the population of Coal village is estimated.The main results are as follows:1.In order to improve the accuracy of residential area extraction,combined with the characteristics of remote sensing image,the normalized band index operation is fully used to divide the remote sensing image band into three groups of band combinations,and the established three groups of band combination images are classified by the minimum distance,maximum likelihood and other commonly used supervised classification methods,and the image calculated by the superimposed normalized index is classified by the maximum likelihood method The accuracy of classification results is higher,and the accuracy of rural residential area classification can reach more than 90%.2?Based on the analysis of the extracted data of population and rural settlements,the area of rural settlements in the study area has a significant positive correlation with the number of rural population.1 Yuan linear regression model is established by using the area and population data.3.In order to improve the accuracy and stability of the estimation model,the control group was used to test each other.To verify the two estimation models,group B(validation group)data is substituted into group A(model group)equation to test the reliability of group A(model group)equation;group A(model group)data is substituted into group B(validation group)equation to test the reliability of group B(validation group)equation.The test results show that the error of the overall residential population of 69 townships tested by group A(model group)is 0.16%,and that of 69 townships tested by group B(verification group)is 0.14%.The overall test effect is ideal.4.According to the distribution characteristics of Jining City and Jining mining area,this paper analyzes the applicability of the population estimation model in Jining coal pressing village,and combines the estimated population error of rural residential area with the mine boundary map.The results show that 60% of the 59 villages and towns within the mine boundary have an error of less than 10%,and 90% of the villages and towns within 20%.Therefore,this model can be used to increase the population of coal pressing village Finally,using this model,the population of the village is estimated rapidly.The research shows that the satellite remote sensing technology is used to estimate the rural population with high accuracy,and saves a lot of time,manpower and material resources,and estimates the population of coal pressure villages,which provides a reference for the relocation of coal pressure villages and related renovation planning.