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

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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