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High-resolution remote sensing image water body extraction method based on low-level feature fusion

A technology of remote sensing images and low-level features, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as low accuracy, misidentification of dark shadows, blurred edges, etc., to achieve accurate water area edges and reduce feature information. The lack of, distinguishing the effect of the excellent effect

Pending Publication Date: 2021-03-26
ZHEJIANG WANLI UNIV
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AI Technical Summary

Problems solved by technology

In recent years, some studies have applied the deep learning technology of convolutional neural network to the extraction of water bodies in remote sensing images, but there are still problems such as low accuracy, blurred edges, and misidentification of dark shadows.

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  • High-resolution remote sensing image water body extraction method based on low-level feature fusion
  • High-resolution remote sensing image water body extraction method based on low-level feature fusion
  • High-resolution remote sensing image water body extraction method based on low-level feature fusion

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Embodiment Construction

[0038] The invention will be further described below with reference to the accompanying drawings and in combination with specific embodiments, so that those skilled in the art can implement it by referring to the description, and the protection scope of the present invention is not limited to the specific embodiments.

[0039]Those skilled in the art should understand that, in the disclosure of the present invention, the terms "vertical", "transverse", "upper", "lower", "front", "rear", "left", "right", " The orientation or positional relationship indicated by "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, which are only for the convenience of describing the present invention and The above terms should not be construed as limiting the present invention because the description is simplified rather than indicating or implying that the device or element referred to must have a specific...

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Abstract

The invention relates to a high-resolution remote sensing image water body extraction method based on low-level feature fusion, which is improved by using a DeepLabv < 3 + > deep learning model by taking the collection of a water body region of a high-resolution remote sensing image as a target, and is used for the extraction work of a high-resolution remote sensing image water body. The loss of feature information in the convolutional neural network is reduced by introducing the work of fusing low-level features and high-level feature maps, and detail feature information is supplemented; training is carried out through a remote sensing image data set with real water body marks, and information supplement of low-layer features is combined, so that the purpose of further distinguishing darkareas such as vegetation and building shadows from water body areas is achieved.

Description

technical field [0001] The invention relates to the technical field of intelligent recognition of remote sensing images, in particular to a method for extracting water bodies from high-resolution remote sensing images based on fusion of low-level features. Background technique [0002] Water is the basic substance in nature to maintain the development of human society. Urban surface water is composed of natural and artificial water. It is an indispensable natural resource in the urban environment and one of the important factors affecting the urban ecological environment. It has a certain impact on urban public health and people's living environment. The formation, expansion, contraction and disappearance of water bodies are important factors affecting regional climate change and ecological environment evolution. Therefore, rapid and accurate extraction and dynamic monitoring of urban surface water bodies have become one of the important tasks of water resources management ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/267G06V10/44G06N3/045G06F18/241
Inventor 王仁芳孙德超吴佳丽邱虹
Owner ZHEJIANG WANLI UNIV
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