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Remote sensing data deep learning based offshore pollutant identifying and tracking method

A deep learning and remote sensing data technology, applied in the field of offshore pollutant identification and tracking, can solve the problems of incomplete discovery, lack of remote sensing data content mining, etc., and achieve the effect of large amount of calculation.

Inactive Publication Date: 2016-06-01
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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Problems solved by technology

[0008] Aiming at the problems of "incomplete discovery" and "lack of content mining of remote sensing data" existing in the existing coastal environment monitoring technology, the present invention proposes a comprehensive, systematic, In-depth solution for offshore pollutant target recognition and tracking based on deep learning of remote sensing data

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  • Remote sensing data deep learning based offshore pollutant identifying and tracking method
  • Remote sensing data deep learning based offshore pollutant identifying and tracking method
  • Remote sensing data deep learning based offshore pollutant identifying and tracking method

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Embodiment Construction

[0027] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0028] The offshore pollutant identification and tracking system based on remote sensing data deep learning of the present invention, as attached figure 1 As shown, it is divided into application layer, content analysis and mining layer, resource data integration layer, resource acquisition layer, including pollutant target identification, decision support subsystem, alarm subsystem, pollutant drift forecast subsystem, various pollution chemical composition and hazard database, pollution cleaning and salvage materials / equipment performance and inventory database, geographic information system, pollution emergency response capability assessment subsystem, pollution damage assessment subsystem, etc., can combine wireless communication system technology to realize ground emergency response center and marine The visualized information communication...

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Abstract

The invention, for the problems of incomplete discovery and lack of remote sensing data content mining in existing offshore environment supervision, based on research and development in the field of satellite application for years and industrial application basis, proposes a comprehensive, systematic and profound remote sensing data deep learning based offshore pollutant target identifying and tracking solution. According to the solution, a satellite remote sensing image deep learning model, massive remote sensing data distributed storage, a parallel processing technology and a GPU acceleration based deep convolutional network parallel model are applied and the application demands of comprehensively, accurately and quickly monitoring marine pollutants in related industries are met.

Description

technical field [0001] The invention belongs to the field of digital image recognition, and in particular relates to a method for recognizing and tracking offshore pollutants. Background technique [0002] In recent decades, with the development of world industry, the pollution of the ocean has become increasingly serious. The pollution caused by the entry of harmful substances into the marine environment will damage biological resources, endanger human health, hinder fishing and other human activities at sea, and damage Seawater quality and environmental quality, etc., have damaged the marine ecosystem. Marine pollutants mainly include petroleum, heavy metals, acids and bases, radionuclides, solid waste, etc. Among them, petroleum pollutants are one of the most common marine pollutants, mainly produced by industry, including offshore oil well pipeline leakage, tanker accidents, Caused by ship sewage, etc., about 10 million tons of oil pollutants are discharged into the oce...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62G06N3/02G06N3/08
CPCG06F16/51G06N3/02G06N3/08G06F18/24
Inventor 王岢徐晓飞叶允明
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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