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Coastline automatic extraction method based on domestic high-score data and deep learning model

A deep learning and coastline technology, applied in the field of remote sensing image processing, can solve problems such as difficulty in achieving classification accuracy, complexity, and difficulty in using useful information from complex data, and achieve the effect of improving accuracy and convenience

Pending Publication Date: 2022-01-28
中国科学院空天信息研究院海南研究院
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Problems solved by technology

However, in high-resolution remote sensing images, due to the large amount of data and its complexity, the existing object-oriented classification methods are difficult to use the useful information hidden in the complex data, and it is difficult to achieve the ideal classification accuracy.

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  • Coastline automatic extraction method based on domestic high-score data and deep learning model
  • Coastline automatic extraction method based on domestic high-score data and deep learning model
  • Coastline automatic extraction method based on domestic high-score data and deep learning model

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Embodiment Construction

[0049] The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0050] In describing the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", " The orientations or positional relationships indicated by "vertical", "horizontal", "top", "bottom", "inner" and "outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and Simplified descriptions, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and thus should not be construed as limiting the scope of the present invention.

[0051] In addition, it should be understood that the use of words such as "first", "second", and "third" to ...

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Abstract

The invention provides a coastline automatic extraction method and device based on domestic high-score data and a deep learning model. The method comprises the steps of fusing a panchromatic image and a multispectral image in satellite image data of a coastline to be extracted to obtain a target fused image; inputting the target fusion image into a pre-trained network model to obtain a sea-land segmented image; and extracting a sea area image to which a sea area in the sea-land segmented image belongs, and obtaining a coastline contained in the satellite image data from the sea area image. According to the method, the coastline is extracted through image fusion and based on the deep learning model, a high-resolution coastline extraction result can be obtained, and the accuracy and convenience of coastline extraction are improved.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to an automatic coastline extraction method based on domestic high-score data and a deep learning model. Background technique [0002] Coastline extraction and change detection are of great significance to coastline resource management, environmental protection, development planning and navigation safety. The rapid development of aerospace remote sensing technology has become a new means of coastline extraction. Its all-weather, large-scale, high-efficiency, and economical advantages have greatly made up for the shortcomings of traditional coastline measurement methods. It has become an important method for monitoring coastline dynamic changes. economic and social benefits. With the development of remote sensing means, the determination of coastline and its change detection have been mostly based on remote sensing technology. Currently, the data sources use...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/13G06V10/26G06V10/774G06K9/62G06N20/00
CPCG06N20/00G06F18/25G06F18/214
Inventor 李慧荆林海
Owner 中国科学院空天信息研究院海南研究院
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