It is well known that Landsat TM images are the most widely used remote sensing data in various fields.Usually,it has 7 different electromagnetic spectrum bands,among which the sixth one has much lower ground resoluti...It is well known that Landsat TM images are the most widely used remote sensing data in various fields.Usually,it has 7 different electromagnetic spectrum bands,among which the sixth one has much lower ground resolution compared with the other six bands.Nevertheless,it is useful in the study of rock spectrum reflection,geothermal resources exploration,etc.To improve the ground resolution of TM6 to the level as that of the other six bands is a problem .This paper presents an algorithm based on the combination of multivariate regression model with semivariogram function which can improve the ground resolution of TM6 by "fusing" the data of other six bands.It includes the following main steps: （1） testing the correlation between TM6 and one of TM15,7.If the correlation coefficient between TM6 and another one is greater than a given threshold value,then select the band to the regression analysis as an argument.（2） calculating the size of the template window within which some parameters needed by the regression model will be calculated; （3） replacing the original pixel values of TM6 by those obtained by regression analysis; （4） using image entropy as a measurement to evaluate the quality of the fused image of TM6.The basic mechanism of the algorithm is discussed and the V C ++ program for implementing this algorithm is also presented.A simple application example is given in the last part of this paper,showing the effectiveness of the algorithm.展开更多
Population is an important strategic resource for national development, a fundamental element of socio-economic development. The coordinated development of population and economy is an effective way to achieve rapid e...Population is an important strategic resource for national development, a fundamental element of socio-economic development. The coordinated development of population and economy is an effective way to achieve rapid economic growth. Based on the population statistics data of counties (districts) in Henan Province, China, from 2006 to 2021. The paper firstly uses the logistic population growth mathematical model to calculate the resident population growth rate of counties (districts), then utilizes the hotspot analysis and spatial semi-variogram analysis, to research the spatial distribution characteristics of the resident population growth rate in Henan Province. The research results show that the evolution of the regional resident population in the province basically conforms to the logistic natural growth model. The resident population growth rate shows the characteristics of high in the north and low in the south, high in the center and low in the surrounding regions. The resident population growth rate is positively correlated with the level of economic development;the urban built-up areas, especially the new regions in urban planning, have a fast growth rate of resident population, which has a significant siphon effect on the population of surrounding regions. The hotspots of resident population growth rate in the province are mainly distributed in the urban built-up areas and surrounding regions of Zhengzhou, Luoyang, and Xinxiang, accounting for about 3.51% of the total area of the province. The cold spots are mainly distributed in the eastern part of the province, forming zonal distribution, which spans across Shangqiu City, Zhoukou City, and Zhumadian City, accounting for about 8.61% of the total area of the province. The area with negative growth of resident population accounts for approximately 53.47% of the total province. The spatial distribution of the growth rate of the resident population in the whole province basically conforms to the spherical model, with a small dispersion degree and a short range. In the range, there is a high degree of variability in resident population growth rate.展开更多
文摘It is well known that Landsat TM images are the most widely used remote sensing data in various fields.Usually,it has 7 different electromagnetic spectrum bands,among which the sixth one has much lower ground resolution compared with the other six bands.Nevertheless,it is useful in the study of rock spectrum reflection,geothermal resources exploration,etc.To improve the ground resolution of TM6 to the level as that of the other six bands is a problem .This paper presents an algorithm based on the combination of multivariate regression model with semivariogram function which can improve the ground resolution of TM6 by "fusing" the data of other six bands.It includes the following main steps: （1） testing the correlation between TM6 and one of TM15,7.If the correlation coefficient between TM6 and another one is greater than a given threshold value,then select the band to the regression analysis as an argument.（2） calculating the size of the template window within which some parameters needed by the regression model will be calculated; （3） replacing the original pixel values of TM6 by those obtained by regression analysis; （4） using image entropy as a measurement to evaluate the quality of the fused image of TM6.The basic mechanism of the algorithm is discussed and the V C ++ program for implementing this algorithm is also presented.A simple application example is given in the last part of this paper,showing the effectiveness of the algorithm.
文摘Population is an important strategic resource for national development, a fundamental element of socio-economic development. The coordinated development of population and economy is an effective way to achieve rapid economic growth. Based on the population statistics data of counties (districts) in Henan Province, China, from 2006 to 2021. The paper firstly uses the logistic population growth mathematical model to calculate the resident population growth rate of counties (districts), then utilizes the hotspot analysis and spatial semi-variogram analysis, to research the spatial distribution characteristics of the resident population growth rate in Henan Province. The research results show that the evolution of the regional resident population in the province basically conforms to the logistic natural growth model. The resident population growth rate shows the characteristics of high in the north and low in the south, high in the center and low in the surrounding regions. The resident population growth rate is positively correlated with the level of economic development;the urban built-up areas, especially the new regions in urban planning, have a fast growth rate of resident population, which has a significant siphon effect on the population of surrounding regions. The hotspots of resident population growth rate in the province are mainly distributed in the urban built-up areas and surrounding regions of Zhengzhou, Luoyang, and Xinxiang, accounting for about 3.51% of the total area of the province. The cold spots are mainly distributed in the eastern part of the province, forming zonal distribution, which spans across Shangqiu City, Zhoukou City, and Zhumadian City, accounting for about 8.61% of the total area of the province. The area with negative growth of resident population accounts for approximately 53.47% of the total province. The spatial distribution of the growth rate of the resident population in the whole province basically conforms to the spherical model, with a small dispersion degree and a short range. In the range, there is a high degree of variability in resident population growth rate.
