Green Tide Information Extraction Based on Multi-source Remote Sensing Data

被引：0

作者：

Liang, Tingting ^{[1
]}

Ke, Lina ^{[1
]}

Fan, Jianchao ^{[2
]}

Zhao, Jianhua ^{[2
]}

机构：

[1] Liaoning Normal Univ, Inst Geog Sci, Dalian, Peoples R China

[2] Natl Marine Environm Monitoring Ctr, Dept Mar Remote Sensing, Dalian, Peoples R China

来源：

2020 12TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2020年

基金：

中国国家自然科学基金;

关键词：

Green tide; optical image; synthetic aperture radar; enhanced vegetation index; OSTU; MACROALGAE BLOOMS; YELLOW SEA; WATERS;

D O I：

10.1109/icaci49185.2020.9177676

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes an approach of green tide information extraction based on multi-source remote sensing data. This method combines different types of high-resolution optical images and synthetic aperture radar (SAR) images. Firstly, it is presented that the texture characteristics of the green tide on different remote sensing images. And then the process of its occurrence and development is analyzed. Enhanced vegetation index (EVI) and OSTU algorithm are used to extract area and distribution of green tide, respectively. In the end, the actual GF-2 optical image and GF-3 SAR image are utilized to verify the effectiveness of the proposed method.

引用

页码：460 / 465

页数：6

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