Sea ice classification method and system based on long-short-term memory network for SAR images

A long-short-term memory and classification method technology, which is applied in the field of SAR image sea ice classification based on long and short-term memory network, can solve the problem of difficulty in obtaining labels for SAR image sea ice classification, achieve the advantage of overall accuracy improvement and reduce errors. The effect of scoring and improving accuracy

Active Publication Date: 2019-01-01
SHANGHAI OCEAN UNIV
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Problems solved by technology

[0012] Sea ice classification based on supervised methods is now the mainstream method, but it is difficult

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  • Sea ice classification method and system based on long-short-term memory network for SAR images
  • Sea ice classification method and system based on long-short-term memory network for SAR images
  • Sea ice classification method and system based on long-short-term memory network for SAR images

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[0053] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0054] In the prior art, the SAR image sea ice classification method based on the long-short-term memory network is not used, and the training samples include the density data of the SAR image classification samples, and the density data of the sea ice is not considered to be as unknown as the sea ice category. factors, resulting in the inability to predict sea ice density.

[0055] The invention proposes a sea ice classification method based on long short-term memory network LSTM of synthetic aperture radar SAR image. In the classification of sea ice in SAR images, considering the time dimension characteristics of sea ice...

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Abstract

The invention belongs to the technical field of sea ice monitoring, a method and system for sea ice classification of SAR image based on long and short time memory network are disclosed, features areextracted directly from SAR image data of sea ice observed continuously as training input of long-term and short-term memory network, and sea ice concentration data are used as one-dimensional features to train the classification network in network training, and the sea ice classification network with both spatial and temporal dimensions is obtained. Considering the unknown sea ice density data, the sea ice density prediction model based on the long-short term memory network is trained first, and then the predicted density data and SAR image data set are input into the trained sea ice classification network to classify the sea ice. In the classification of sea ice in SAR image, the invention considers the time dimension characteristic of the sea ice type change in the time evolution process, and the recognition rate of the similar sea ice type is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of sea ice monitoring, and in particular relates to a SAR image sea ice classification method and system based on a long short-term memory network. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] According to the sea ice classification standard of the World Meteorological Organization, sea ice can be divided into new ice, gray ice, gray ice, one-year ice, old ice, and multi-year ice according to the development state. Synthetic Aperture Radar (SAR) has the characteristics of all-day, all-weather, multi-angle, and strong penetration, and is the main tool for sea ice monitoring. Also a source of image data for sea ice classification based on image features. [0004] At present, sea ice classification methods based on SAR images can be divided into two categories: 1. Feature-based sea ice classification, such as extracting the polariza...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/241
Inventor 宋巍黄冬梅李明慧王振华王建郑小罗
Owner SHANGHAI OCEAN UNIV
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