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Remote sensing image road information extraction method and device based on convolutional neural network

A convolutional neural network and remote sensing image technology, applied in the field of remote sensing image road information extraction, can solve the problems of consumption, inaccurate road extraction, low extraction efficiency, etc., and achieve the effect of accurate road information from remote sensing images and good road information extraction ability.

Active Publication Date: 2021-04-30
INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
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Problems solved by technology

The end-to-end method needs to build an encoding and decoding CNN to train and learn on small images, and then finely extract roads. However, this method not only requires a large amount of training sample data, but also needs to establish a complex model and a deep network level. It consumes a huge time cost, and there are inaccurate road extraction and low extraction efficiency
The method based on pixel blocks needs to construct CNN to predict pixels one by one in the form of a sliding window, which is suitable for large-scale road information extraction. Although this method fully considers the spatial information of roads, it is prone to occurrence due to the influence of buildings and other features. Misclassification

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  • Remote sensing image road information extraction method and device based on convolutional neural network
  • Remote sensing image road information extraction method and device based on convolutional neural network
  • Remote sensing image road information extraction method and device based on convolutional neural network

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[0043]The present invention will be described in detail below with reference to the accompanying drawings and examples. Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, those skilled in the art will recognize that modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield a still further embodiment. Therefore, it is intended that the present invention includes such modifications and variations as come within the scope of the appended claims and their equivalents.

[0044] The method for extracting road information from remote sensing images based on convolutional neural network in this application is as follows: figure 1 As shown, first preprocess the initial remote sensing image data of the research area to obtain sample data; the sampl...

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Abstract

The invention provides a remote sensing image road information extraction method and device based on convolutional neural network. The method comprises the steps of firstly obtaining initial remote sensing image data of a research area; preprocessing the initial remote sensing image data to obtain sample data, wherein the sample data comprises sample training data and sample test data; obtaining a CNN network model according to the sample training data and a pre-constructed convolutional neural network framework model, wherein the CNN network model comprises the pre-constructed convolutional neural network framework model; obtaining initial road information according to the sample test data and the CNN network model; and filtering the initial road information, and extracting to obtain the remote sensing image road information. The method has good road information extraction capability, the road can be effectively and accurately extracted with high performance, and the obtained remote sensing image road information is more accurate and reliable.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method and device for extracting road information from remote sensing images based on a convolutional neural network. Background technique [0002] High-resolution remote sensing images contain relatively rich and detailed ground object information, and are widely used as data sources in various fields of geosciences such as land use planning and water body road extraction. As the main surface object in high-resolution remote sensing images, road is an important carrier of urban emergency response, route planning, traffic navigation, resource allocation, etc., and its significance is self-evident. Extracting road information from high-resolution remote sensing images is not only time-saving and labor-saving, but also large in volume and wide in scope compared with traditional surveying and mapping methods. However, how to accurately and effectively extract...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04
CPCG06V20/194G06V20/182G06V20/13G06V10/454G06N3/048G06N3/045G06F18/24G06F18/214
Inventor 王勇
Owner INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
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