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Multilayer ConvLSTM sea surface temperature prediction calculation method based on remote sensing data

A technology of remote sensing data and calculation methods, applied in the direction of prediction, calculation, data processing applications, etc., can solve problems such as poor results, achieve high prediction accuracy, quick use, and reduce manual effects

Pending Publication Date: 2021-03-23
SHANGHAI OCEAN UNIV
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Problems solved by technology

Based on the traditional time series network, which does not combine time and space well, it is less effective for batch processing of sea surface temperature data for many years

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  • Multilayer ConvLSTM sea surface temperature prediction calculation method based on remote sensing data
  • Multilayer ConvLSTM sea surface temperature prediction calculation method based on remote sensing data
  • Multilayer ConvLSTM sea surface temperature prediction calculation method based on remote sensing data

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Embodiment Construction

[0037] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following the described embodiment.

[0038] With the rapid development of global science and technology, countries are competing to launch their own remote sensing satellites. With the continuous increase of "star sources", remote sensing information technology provides an effective means of multi-source and multi-scale spatio-temporal information for humans to understand the ocean world, and provides abundant data for ocean temperature monitoring, forecasting and scientific research. Especially in the acquisition of long-term global ocean temperature data, it has advantages that cannot be replaced by co...

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Abstract

The invention discloses a multilayer ConvLSTM sea surface temperature prediction and calculation method based on remote sensing data, belongs to the technical field of ocean remote sensing, combines the characteristics of a convolutional neural network and a recurrent neural network, can effectively process the batch problem of time sequence remote sensing data, predicts the seasonality and annuallong-term trend of sea surface temperature, and improves the prediction accuracy of sea surface temperature. Spatial information elements are displayed through time information, and spatial featuresof future sea surface temperature under periodic changes are predicted. The method comprises the following steps: receiving satellite sea surface temperature remote sensing data; preprocessing the remote sensing data; taking the normalized value as a label value of the generator; establishing a sample generator; and randomly selecting the data in the generator for training. According to the methodfor predicting the sea surface temperature based on the remote sensing data, the batch problem of the time sequence remote sensing data can be effectively solved, and the seasonality and the annual long-term trend of the sea surface temperature can be predicted.

Description

technical field [0001] The invention relates to the technical field of marine remote sensing, in particular to a multi-layer ConvLSTM sea surface temperature prediction calculation method based on remote sensing data. Background technique [0002] Sea temperature is one of the most fundamental environmental parameters of the ocean. Seawater temperature has a wide range of applications in fishery resources, monitoring of marine environmental pollution, ocean dynamics, and air-sea interaction. The measurement of sea surface temperature usually includes on-site measurement and satellite remote sensing. The on-site measurement data mainly comes from ship measurements and measurements from fixed or mobile buoy networks. These data play a key role in correcting satellite data and providing a large amount of measured temperature data. However, its coverage is limited, and the seawater temperature data obtained are only a few points or sections, so it is impossible to realize the s...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/049G06N3/08G06N3/045Y02A90/10
Inventor 韩震张雪薇周玮辰
Owner SHANGHAI OCEAN UNIV
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