Urban mapping method and device

Abstract

The invention discloses an urban mapping method and device. The urban mapping method comprises the following steps of receiving a remote sensing image of a target city input by a user and obtaining remote sensing image data; obtaining a target vegetation index of the remote sensing image based on the remote sensing image data; receiving original light data of the target city input by the user; carrying out normalization and re-sampling processing on the original light data to obtain target light data; obtaining a city index based on the target vegetation index and the target light data; and carrying out mapping based on the city index to obtain a city effect drawing of the target city. The urban mapping method has simple and convenient processing process, high automation degree and high processing efficiency and does not need a complicated preprocessing process; and furthermore, the city index is obtained based on the target vegetation index and the target light data, the city index is utilized to carry out mapping, the mapping effect is good, the precision is high and the method is easy to popularize and apply.

Description

technical field [0001] The invention relates to the technical field of remote sensing applications, in particular to a method and device for urban mapping. Background technique [0002] Cities, due to the concentration of population and resources, have become the focus of research on global climate change and human activities. Although the city occupies only 2% of the land area, it occupies 50% of the population, produces 90% of GDP, and emits 70% of greenhouse gases. In order to better monitor the impact of urban development on human beings, a large number of studies The scale range and high-precision monitoring level have become a hot issue for exploration. [0003] Due to the wide coverage of remote sensing imagery, it has become an important data source for monitoring urban development. Low spatial resolution remote sensing imagery can cover a large area, but due to its low spatial resolution, it cannot show the details of the city. At present, the urbanization researc...

AI Technical Summary

Problems solved by technology

[0003] Due to the wide coverage of remote sensing imagery, it has become an important data source for monitoring urban development. Low spatial resolution remote sensing imagery can cover a large area, but due to its low spatial resolution, it cannot show the details of the city.

At present, the research on urbanization based on remote sensing images mainly has the following areas to be strengthened: the definition of cities is not clear, the spatial resolution of remote sensing images is too low, and the existing algorithms are not effective enough

[0004] Because of its higher spatial resolution (30m), Landsat remote sensing images have become a key data source for the study of urbanization. However, Landsat remote sensing images only use optical images, and it is difficult to improve the phenomenon of the same spectrum and different objects, such as naked For problems such as land and buildings, shadows and water bodies, relevant researchers have proposed processing methods based on indices such as biomass component index and multi-source normalized impervious index.

However, in the processing method based on the biomass component index, the water body needs to be masked in the preprocessing stage, and there are many preprocessing steps, and the operation is complicated, which seriously affects the calculation efficiency

In the processing method based on the multi-source normalized impermeability index, it has the disadvantages of small coverage of nighttime images and large error of surface temperature, which leads to low accuracy of the urban map area and cannot display the details of the city.

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