Urban impervious surface coverage rate remote sensing estimation method

Abstract

The invention discloses an urban impervious surface coverage rate remote sensing estimation method. According to the urban impervious surface coverage rate remote sensing estimation method, medium resolution remote sensing images high in cost performance are taken as the main data sources, chaos particle swarm optimization support vector regression is adopted for estimation of urban impervious surface coverage rate; improvement on parametric optimization algorithm in support vector regression is carried out, an improved support vector regression UISP remote sensing estimation method applied tomedium resolution remote sensing images is promoted so as to increase precision of support vector regression in medium resolution remote sensing image UISP estimation. The performance of UISP estimation taking the medium resolution remote sensing image as the main data sources is improved further, and more accurate urban impervious surface data is provided for correlation research taking urban impervious surface information as input data.

Description

technical field [0001] The present invention relates to the detection of urban impermeable layer, more specifically, relates to a method for remote sensing estimation of coverage rate of urban impermeable layer. Background technique [0002] Urban Impervious Surfaces (Urban Impervious Surfaces, UIS) is an important artificial surface in the city. It can prevent groundwater from seeping into the soil and cut off the connection between the urban surface and underground hydrology. fields and asphalt roads. Urban impermeable layer is not only an indicator to measure the degree of urbanization, but also an important indicator to measure the quality of urban environment. With the continuous acceleration of the global urbanization process, the proportion of urban impervious layers has gradually expanded, which has caused a series of ecological, environmental and social problems such as urban heat island effect, environmental pollution, and resource shortage. Therefore, extracting...

AI Technical Summary

Problems solved by technology

However, the GA algorithm has problems such as complex implementation, large dependence on initial values, and limited parallelism.

Although the PSO algorithm has the characteristics of fast convergence and simple rules, its shortcomings such as premature convergence and easy local optimum cannot be ignored.

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