Functional area identification method based on remote sensing image and

Abstract

The invention relates to a functional area identification method based on remote sensing image and POI. The data used includes a second high-score remote sensing image, network POI point data and field sampling data. The processing process is as follows: a, preprocessing the remote sensing image to unify the coordinate system of multi-source data; B, classifying that city function and POI, and classifying the POI lay as a function layer; (c) segmentating and classifying a remote sensing image to obtain the segmented vector data and the land use data of the non-built-up area; (d) adding that POI data to the broken plaque according to the rule to obtain the broken functional plaque; E, adding the broken patch to the block patch according to the weight value and the calculation formula, and obtaining the urban functional zoning with the block as the unit.

Description

technical field [0001] The invention belongs to the application field of remote sensing information technology in urban geography, and focuses on solving the problem of identification of urban internal functional areas. It identifies urban functional areas based on remote sensing images and network POI data, and solves the problem that remote sensing images cannot accurately identify urban internal land use. At the same time, it uses POI data is surface-shaped and has boundary attributes. The combined use of the two data can objectively, quickly and accurately identify urban functional areas. It can be used not only for the dynamic study of urban built-up areas, but also for the study of related issues within the city. Background technique [0002] Remote sensing images have been widely used in related studies such as land cover on the earth's surface, air quality, and temperature inversion. In urban geography, remote sensing images are mainly used to extract urban land use t...

AI Technical Summary

Problems solved by technology

[0003] At present, in urban research based on remote sensing images, TM images are used to study large-scale urban expansion. In urban interiors, high-resolution remote sensing images are used to analyze non-built-up areas and their dynamic changes. The attributes of buildings such as services are difficult to determine from remote sensing images, so the accuracy of remote sensing images to identify objects with social attributes is low; there are relatively few applications and analyzes of POI data. Currently, the main methods used in the research of urban built-up areas are Calculate the density of points within the road buffer zone, and study the distribution characteristics of urban functional areas along the road direction

The method based on point density has certain shortcomings. Due to the large gap between different types of function point densities, in the process of constructing the function weight index system, it is difficult to determine a unified standard for each type of function weight, which affects the accuracy of functional area identification.

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