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A method, system and medium for water body recognition based on deep learning

A technology of deep learning and recognition methods, applied in the field of water body recognition based on deep learning, can solve problems such as no algorithm generalization performance test

Active Publication Date: 2022-04-15
AEROSPACE INFORMATION RES INST CAS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the above methods have not tested the generalization performance of the algorithm on large-scale multi-temporal SAR data.

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  • A method, system and medium for water body recognition based on deep learning
  • A method, system and medium for water body recognition based on deep learning
  • A method, system and medium for water body recognition based on deep learning

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Embodiment Construction

[0029] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the present disclosure will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Embodiments of the present disclosure will be described in further detail below in conjunction with the accompanying drawings and specific embodiments, but are not intended to limit the present disclosure. For the various steps described herein, if there is no need for a contextual relationship between each other, the order described herein as an example should not be considered as a limitation, and those skilled in the art will know that the order can be adjusted, as long as It is enough not to destroy the logic between them so that the whole process cannot be realized.

[0030] The first aspect of the present disclosure provides a water body recognition method based on deep learning. figure 1 A flow chart showing a water body ...

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Abstract

The present disclosure relates to a water body recognition method, system and medium based on deep learning. Wherein the method includes: Step S1, obtaining a water body sample set, using images in the water body sample set to obtain a network model for identifying the water body through training, the network model is a U-shaped full convolutional neural network model, Including a downsampling convolutional coding layer and an upsampling convolutional coding layer, each convolutional coding layer is connected through a ResNet residual structure; step S2, receiving a water body image to be identified, and preprocessing the water body image, the water body The image is a medium-resolution large-area SAR image; and step S3, using the network model to identify the preprocessed water body image. The method realizes high-precision, automatic water body identification and multi-temporal monitoring by constructing a robust water body identification network model.

Description

technical field [0001] The present disclosure relates to the field of image processing, and more specifically, to a water body recognition method, system and medium based on deep learning. Background technique [0002] Timely and accurate large-scale mapping and dynamic monitoring of water bodies is of great significance for water resource monitoring and flood control and disaster mitigation. Synthetic aperture radar (SAR) has the characteristics of all-day, all-weather, and large-scale periodic earth observation, and plays a key role in the application of large-scale water body monitoring. However, there are still some challenges in large-scale, high-efficiency and high-precision water body identification and dynamic monitoring. [0003] The emergence of remote sensing provides advanced technical means for the acquisition of flood information. With the increase of remote sensing data sources, the research on using optical sensor data to obtain surface water body informati...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/10G06V10/774G06V10/764G06V10/82G06V10/26G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/182G06V10/267G06N3/045G06F18/24G06F18/214
Inventor 吴樊李娟娟王超张红张波
Owner AEROSPACE INFORMATION RES INST CAS