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Building identification and building height inversion calculation method and system

A technology of building height and calculation method, which is applied in the field of building identification and building height inversion calculation method and system, which can solve the problems of low building identification accuracy, low building identification efficiency, and blurred and inaccurate building edge identification. Accuracy and other issues to solve the effect of low recognition efficiency

Pending Publication Date: 2022-07-22
UNIV OF SCI & TECH BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a building recognition and building height inversion calculation method and system to solve the problems of low building recognition efficiency and fuzzy and inaccurate building edge recognition in the existing building recognition technology, which lead to building The technical problems of low object recognition accuracy and difficulty in obtaining building heights

Method used

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  • Building identification and building height inversion calculation method and system
  • Building identification and building height inversion calculation method and system
  • Building identification and building height inversion calculation method and system

Examples

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no. 1 example

[0052] This embodiment provides a building identification and building height inversion calculation method, the method can be realized by electronic equipment, and the execution flow of the method is as follows figure 1 shown, including the following steps:

[0053]S1, obtain high-resolution remote sensing images of buildings containing shadows, and construct a data set of buildings and shadows;

[0054] S2, the U-Net network is improved according to the preset method, and the detection model of buildings and shadows is obtained;

[0055] S3, based on the data set, the detection model of buildings and shadows is trained;

[0056] S4, use the trained detection model to detect and extract the buildings and shadows in the remote sensing image to be detected, and obtain the buildings and shadows in the remote sensing image to be detected currently;

[0057] S5, perform height inversion calculation on the building based on the shadow detection and extraction result to obtain the ...

no. 2 example

[0097] This embodiment provides a building identification and building height inversion calculation system, the building identification and building height inversion calculation system includes the following modules:

[0098] A dataset building module for obtaining high-resolution remote sensing images of buildings containing shadows, and constructing datasets of buildings and shadows;

[0099] The model building module is used to improve the U-Net network according to the preset method to obtain the detection model of buildings and shadows;

[0100] A model training module for training the building and shadow detection models constructed by the model building module based on the data set constructed by the data set construction module;

[0101] The detection module is used for using the detection model trained by the model training module to detect and extract the buildings and shadows in the remote sensing images to be detected, and obtain the buildings and shadows in the cu...

no. 3 example

[0107] This embodiment provides a computer device, Figure 8 A block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 8 The computer device 12 shown is only an example, and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention. The computer device 12 may be any terminal device with computing capability, such as an intelligent controller, a server, a mobile phone and other terminal devices.

[0108] like Figure 8 As shown, computer device 12 takes the form of a general-purpose computing device. Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16 , system memory 28 , and a bus 18 connecting various system components including system memory 28 and processing unit 16 .

[0109] Bus 18 represents one or more of several types of bus structures, including a memory bus or memo...

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Abstract

The invention discloses a building identification and building height inversion calculation method and system, and the method comprises the steps: obtaining a high-resolution remote sensing image of a building containing a shadow, and constructing a data set of the building and the shadow; improving the U-Net network according to a preset mode to obtain a building and shadow detection model; training a building and shadow detection model based on the data set; detecting and extracting the building and the shadow in the remote sensing image to be detected by using the trained detection model to obtain the building and the shadow in the current remote sensing image to be detected; and performing height inversion calculation on the building based on a shadow detection extraction result to obtain the height of the building. According to the building identification and building height inversion calculation method, deep learning and shadow-based height inversion calculation are utilized, so that the building identification precision and the building height calculation efficiency are improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a building identification and building height inversion calculation method and system. Background technique [0002] With the rapid development of aerospace technology, sensor technology, and remote sensing technology, not only can high-resolution, multi-spectrum, and multi-temporal satellite remote sensing images be obtained in a timely and rapid manner, but also large-scale, high-precision, quasi-real-time remote sensing images can be continuously obtained. Land feature information. As deep learning has become a cutting-edge research direction in the field of computer machine learning, it has achieved impressive results in natural language processing and computer vision in recent years. Using deep learning technology to achieve automatic detection and recognition of satellite imagery targets, its generalization The capability and detection accuracy are ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/13G06T5/40G06T5/00G06N3/08G06N3/04
CPCG06T7/0002G06T7/13G06N3/08G06T7/11G06T5/40G06T2207/20132G06T2207/10032G06N3/045G06T5/90
Inventor 王莉黄鹏栗辉张德政肖成勇
Owner UNIV OF SCI & TECH BEIJING
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