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Analysis Method of Road Traffic Situation in Remote Sensing Image Based on Fuzzy Neural Network

A fuzzy neural network and road traffic technology, applied in the field of road traffic analysis of remote sensing images, can solve the problems of inability to road traffic, decreased accuracy, excessive manual intervention, etc., to facilitate traffic analysis and avoid incomplete road information. Effect

Active Publication Date: 2018-10-02
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to solve the problems of the accuracy decline caused by too much manual intervention and the inability to analyze road traffic conditions in a large area in the existing methods, and proposes a method for analyzing road traffic conditions of remote sensing images based on fuzzy neural networks

Method used

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  • Analysis Method of Road Traffic Situation in Remote Sensing Image Based on Fuzzy Neural Network
  • Analysis Method of Road Traffic Situation in Remote Sensing Image Based on Fuzzy Neural Network
  • Analysis Method of Road Traffic Situation in Remote Sensing Image Based on Fuzzy Neural Network

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specific Embodiment approach 1

[0020] Specific implementation mode one: as figure 1 As shown, the method for analyzing road traffic conditions of remote sensing images based on fuzzy neural network includes the following steps:

[0021] Step 1: Determine the input parameters as the number of lanes, vehicle type, vehicle density and vehicle speed, and normalize the input parameters;

[0022] Determining factors: ①It is relatively easy to obtain from remote sensing images; ②It has a relatively direct relationship and influence on road traffic conditions; ③It is relatively easy to quantify the mathematical model so that it can be used as the input of the fuzzy neural network system. Finally, the input parameters are determined as follows: vehicle type, number of one-way lanes, traffic density, and vehicle speed.

[0023] Step 2: Determine the traffic conditions of the road as smooth, mildly congested, congested and severely congested;

[0024] Step 3: Determine the rules between input parameters and road tra...

specific Embodiment approach 2

[0029] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the specific process of normalizing the input parameters in Step 1 is as follows:

[0030] The normalization method used is the max-min method:

[0031]

[0032] where the x min is the minimum value in the training sample data, x max is the maximum value in the training sample data, x k is the normalized input parameter;

[0033] The normalized input parameters enter the network as the input quantity of the fuzzy neural network to form the characteristic input vector of the network.

[0034] Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0035] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in the described step two, determine that the road traffic conditions are smooth, mildly congested, congested and severely congested. The four situations are specifically:

[0036] Assumption: the severe congestion value is 4, the congestion value is 3, the light congestion value is 2, and the unblocked value is 1. For a certain input parameter, the weight of the most likely traffic situation is set to 2, and the weight of the most likely traffic situation is set to 1, so that the weighted average of the road traffic conditions finally determined by various input parameters is carried out, and finally Obtain the possible traffic condition value of the target road. The calculated value is in the range of 1 to 4, and finally the traffic condition of the road is determined in the following range:

[0037] If the traffic situation value is in the range of [1, 1.5), the ...

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Abstract

The present invention relates to a remote sensing image road traffic situation analysis method, in particular, a fuzzy neural network-based remote sensing image road traffic situation analysis method. The objective of the invention is to solve the problem of accuracy decrease caused by excessive manual intervention and incapability of carrying out road traffic situation analysis in a large-range area in an existing method. The method of the invention includes the following steps that: (1) input parameters are determined, and normalization processing is performed on the input parameters; (2) a road traffic situation is determined; (3) rules between the input parameters and the road traffic condition are determined; (4) a fuzzy neural network system for road traffic situation analysis is constructed; (5) training samples and test samples are collected, all the training samples are clustered, and the neural network system is trained; (6) required road attribute information is obtained; and (7) the road attribute information is inputted into the network, so that traffic situation analysis is carried out, an obtained analysis result is compared with an actual traffic situation in an image, so that the reliability of the network can be verified. The method of the invention is applied to the high-resolution remote sensing image analysis field.

Description

technical field [0001] The invention relates to a method for analyzing road traffic conditions of remote sensing images. Background technique [0002] As an important means of obtaining surface information, remote sensing technology has been applied in many fields, such as land cover changes, disasters, Monitoring of the environment and resources. In recent years, with the rapid development of remote sensing technology, the resolution of remote sensing images has been increasing. Road is an important geographic information in remote sensing images, which can be used as clues and references for extracting other ground targets. Therefore, accurate extraction of road information is of great significance to the further application of remote sensing images. On the actual road, there are many factors that affect traffic conditions, such as lane width, number of lanes, vehicle speed, etc. If these data are collected manually and theoretical calculations are performed to judge th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01
CPCG08G1/012G08G1/0125
Inventor 陈浩张晔李冬青范婷婷
Owner HARBIN INST OF TECH
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