Study on Monitoring of Red Tide by Multi-Spectral Remote Sensing Based on HJ-CCD and MODIS

被引：9

作者：

Wang, Ganlin ^{[1
,3
]}

Zhang, Bing ^{[1
,2
]}

Li, Junsheng ^{[2
]}

Zhang, Hao ^{[2
]}

Shen, Qian ^{[2
]}

Wu, Di ^{[4
]}

Song, Yang ^{[4
]}

机构：

[1] E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200062, Peoples R China

[2] Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R China

[3] Shanghai Ocean Univ, Coll Informat Technol, Shanghai, Peoples R China

[4] Chinese Acad Sci, Grad Sch, Beijing, Peoples R China

来源：

2011 2ND INTERNATIONAL CONFERENCE ON CHALLENGES IN ENVIRONMENTAL SCIENCE AND COMPUTER ENGINEERING (CESCE 2011), VOL 11, PT C | 2011年 / 11卷

基金：

中国国家自然科学基金;

关键词：

Red tide; Remote Sensing; Multi-spectral; HJ-CCD; MODIS;

D O I：

10.1016/j.proenv.2011.12.235

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In order to monitor red tide in Case 2 waters, this paper proposes a method of multi normalized difference indices combination to extract red tide information from multi-spectral remote sensing image, and uses HJ-CCD and MODIS data to extract red tide information in the Shenzhen near-shore waters and the Pearl River Estuary to verify its feasibility. Because the spatial resolution of HJ-CCD is better than MODIS, the extract result is better than MODIS. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Intelligent Information Technology Application Research Association.

引用

页码：1561 / 1565

页数：5

共 50 条

[41] Detecting the red tide based on remote sensing data in optically complex East China Sea
Xu, Xiaohui
Pan, Delu
Mao, Zhihua
Tao, Bangyi
Liu, Qiong
REMOTE SENSING OF THE OCEAN, SEA ICE, COASTAL WATERS, AND LARGE WATER REGIONS 2012, 2012, 8532
[42] Thin Cloud Removal from Multi-Spectral Remote Sensing Images Based on the Physical Model of Thin Cloud
Shao, Shuai
Yu, Da
Yuan, Hangfei
Liu, Chunxiang
Chen, Xinran
TRAITEMENT DU SIGNAL, 2025, 42 (01) : 177 - 182
[43] A Noise-Robust SVD-ML Based Classification Method for Multi-Spectral Remote Sensing Images
Zehtabian, Amin
Ghassemian, Hassan
2013 21ST IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2013,
[44] Structural dynamics analysis of dual-channel multi-spectral remote sensing camera
Zhang Nan
Chang Junlei
Li Qinglin
AOPC 2019: OPTICAL SENSING AND IMAGING TECHNOLOGY, 2019, 11338
[45] Multi-spectral Gradient Residual Network for Haze Removal in Multi-sensor Remote Sensing Imagery
Yang, Xian
Vatsavai, Ranga Raju
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES-APPLIED DATA SCIENCE TRACK, PT X, ECML PKDD 2024, 2024, 14950 : 413 - 428
[46] Saliency Detection for Ship Targets on Four-Band Multi-Spectral Remote Sensing Images
Wang Wensheng
Huang Min
Li Tianjian
Hu Huan
Bi Guoling
ACTA OPTICA SINICA, 2020, 40 (17)
[47] Estimation of broadband land surface emissivity from multi-spectral thermal infrared remote sensing
Ogawa, K
Schmugge, T
Jacob, F
French, A
AGRONOMIE, 2002, 22 (06): : 695 - 696
[48] Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night
Li, Siyuan
Jiao, Jiannan
Wang, Chi
REMOTE SENSING, 2021, 13 (17)
[49] Measurement, Correction and Validation of Out-of-Band Response for Multi-spectral Remote Sensing Instruments
Yuan, Yinlin
Zheng, Xiaobing
Wu, Haoyu
Zhai, Wenchao
Qian, Honghu
Luo, Donggen
Yang, Weifeng
Hong, Jin
3RD INTERNATIONAL SYMPOSIUM OF SPACE OPTICAL INSTRUMENTS AND APPLICATIONS, 2017, 192 : 269 - 278
[50] MULTI-SPECTRAL BAND SELECTION AND SPATIAL EXPLANATIONS USING XAI ALGORITHMS IN REMOTE SENSING APPLICATIONS
Temenos, Anastasios
Temenos, Nikos
Kaselimi, Maria
Doulamis, Anastasios
Doulamis, Nikolaos
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 6093 - 6096

← 1 2 3 4 5 →