Coastline deep learning remote sensing extraction method based on coupling atlas features
A deep learning and extraction method technology, applied in the field of remote sensing detection, can solve the problems of large errors and low precision, achieve comprehensive monitoring and extraction, improve extraction accuracy, precise extraction and promote the effect of coastal disaster prevention and control
Pending Publication Date: 2022-03-01
陕西九州遥感信息技术有限公司 +3
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[0008] In order to solve the defects of large errors and low precision in existing remote sensing image coastline extraction methods, the present invention proposes a coastline deep learning remote sensing extraction method based on coupling map features to realize fast, accurate and comprehensive monitoring and extraction of coastlines
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[0042] In order to understand the above-mentioned purpose, features and advantages of the present invention more clearly, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways than those described here. Therefore, the present invention is not limited to the specific embodiments disclosed below.
[0043] This embodiment proposes a coastline deep learning remote sensing extraction method based on coupled map features, aiming to solve the technical problems of slow automatic coastline extraction, low extraction accuracy, and difficult monitoring, such as figure 1 s...
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The invention discloses a coastline deep learning remote sensing extraction method based on coupling atlas features, and the method comprises the steps: selecting four wavebands, namely, RGB and a near-infrared waveband, from a remote sensing image, building a deep learning network model, and transforming the structure of the model; the model automatically compares and analyzes the sea-land binary image obtained by training the training set sample with the marked sea-land binary image, and then carries out back propagation network optimization and self-learning to obtain a sea-land binary image network model; and then inputting the remote sensing image into the sea-land binary image network model, performing quality control on the input remote sensing image by the sea-land binary image network model, performing vectorization and coastline generation operation on the obtained sea-land segmented region binary image, and finally obtaining the coastline of the remote sensing image of the coastline region. According to the method, a map feature coupling mode is utilized, on one hand, the problem that the coastline extraction precision is low is solved, on the other hand, the coastline extraction speed is increased, and support is provided for automatically and efficiently extracting the high-resolution image coastline.
Description
technical field [0001] The invention relates to the technical field of remote sensing detection, in particular to a remote sensing extraction method of coastline deep learning based on coupling map features. Background technique [0002] With the rapid economic development of coastal areas, the speed of urbanization has accelerated, but it has also caused a series of ecological related problems, such as: the natural coastline shrinks, the artificial coastline increases, and the shape of the coastline changes accordingly. What's more, due to cost savings Leading to the disappearance of natural bays; causing the deterioration of the ecological environment and water quality of the coastal zone, aggravating seawater pollution, and the ecological environment of the coastal zone has become more and more fragile. [0003] Remote sensing is an emerging technology developed in the late 1950s to observe the earth and explore the universe from space. As a high-tech, remote sensing has...
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Login to View More IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06T7/11G06N3/084G06T2207/10024G06T2207/10036G06T2207/20081G06T2207/20084G06T2207/20132G06T2207/30204G06N3/045Y02A10/40
Inventor 田森叶秋果陈军蔺楠孙记红
Owner 陕西九州遥感信息技术有限公司