A remote sensing image water body extraction method and system based on deep learning

A remote sensing image and deep learning technology, applied in the field of remote sensing image processing, can solve the problems of large changes in different images, low level of automation and intelligence, and inability to meet high spatial resolution image automation, etc., to achieve improved intelligence and high precision The effect of extraction

Inactive Publication Date: 2019-06-25
中交信息技术国家工程实验室有限公司
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Problems solved by technology

[0004] Existing remote sensing methods for water body extraction mainly rely on complex spectral analysis limited by experience and the optimal threshold value obtained through extensive training, and the optimal threshold value varies greatly for different images, and the overall level of automation and intelligence is not high. Meet the requirements of high spatial resolution image automation, fast processing of high-precision images, and accurate detection of water bodies

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  • A remote sensing image water body extraction method and system based on deep learning
  • A remote sensing image water body extraction method and system based on deep learning
  • A remote sensing image water body extraction method and system based on deep learning

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[0054] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0055] Deep learning is the use of computers to simulate human learning behaviors, acquire new knowledge and skills, reorganize existing knowledge structures and continuously optimize knowledge bases, and finally make optimal decisions. Deep learning image recognition is to use the convolutional neural network to randomly select a small area from the image as a training sample, learn some features of characteristic information from the sample, and then use these features as filters to operate with the original image, so that The activation value of different features at any position in the original image is obtained, and then the value is input into the classifier fo...

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Abstract

The invention discloses a remote sensing image water body extraction method and system based on deep learning. By utilizing China's high score-No.1/No.2 (GF-1/GF-2) and resource-No.3 (ZY-3) satelliteimages and two kinds of deep learning convolution neural networks (CNNs) of U-net and Densenet, a novel CNNs model for extracting water bodies is provided. By labeling water bodies in a large number of high-resolution remote sensing images, obtaining water body labeling results in the images to serve as a training set; Carrying out a training test on the high-resolution remote sensing image by using the established CNNs model to obtain a binary image of the water body; And identifying the pixel value identified in the binary image to obtain the water body target. According to the method, the water body information in the high-resolution remote sensing image can be quickly extracted in a high-precision and automatic manner, the intelligence, automation level and extraction precision of water body extraction are improved, and the method can be widely applied to various fields related to water body extraction.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a method and system for extracting water bodies from remote sensing images based on deep learning. Background technique [0002] With the development of my country's aerospace industry, more and more ground object information is obtained by earth observation, and the image resolution obtained by satellite images and aerial images is also getting higher and higher. Extracting water bodies on the Earth's surface has always been a fundamental task in remote sensing image analysis. As a key component of the hydrological cycle, the extent of water is critical for monitoring extreme events and understanding the mechanisms of climate change. [0003] Regionally, changes in water have major impacts on ecosystems and human life. Accurate perception of water level status is critical for successful quantitative assessment of spatiotemporal monitoring, change detect...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04
Inventor 康婧慈天宇苏航耿丹阳
Owner 中交信息技术国家工程实验室有限公司
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