Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2009, Vol. 21 Issue (2) : 41-44     DOI: 10.6046/gtzyyg.2009.02.09
Technology and Methodology |
REMOTE SENSING MONITORING OF AQUACULTURE AND AUTOMATIC
INFORMATION EXTRACTION
GUAN Xue-bin1,2|ZHANG Cui-ping3|JIANG Ju-sheng4|CAO Jian-hua1
1.Rubber Research Institute|CATAS|Danzhou 571737|China; 2.Environment and Plant Protection Institute of
Hainan University, Danzhou 571737,China|3.Environment Science Research Institute of Hainan,Haikou 570206,China;
4.Innovation Center of Science and Technology|Hainan State Farm Bureau|Haikou 570206,China
Download: PDF(2053 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Aquaculture constitutes one of the important agricultural production means in China and has a significant

irreplaceable effect on the development of economy. The monitoring and scientific management of aquaculture is

therefore especially important to the people. At present, researches on the remote sensing monitoring of aquaculture

are very insufficient both at home and abroad. With Wenchang area of Hainan Province as an example, the authors

tentatively extracted  the aquaculture area by means of remote sensing. The object-oriented classification method

was adopted in the monitoring and, as a result, an ideal result was obtained. Some suggestions are put forward for

further improving the classification precision.

Keywords Hyperspectral data      Processing methods       Characteristic information extraction      Metallogenic prediction     
: 

P 75

 
Issue Date: 12 June 2009
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Cite this article:   
GUAN Xua-Bin, ZHANG Cui-Ping, JIANG Ju-Sheng, CAO Jian-Hua. REMOTE SENSING MONITORING OF AQUACULTURE AND AUTOMATIC
INFORMATION EXTRACTION[J]. REMOTE SENSING FOR LAND & RESOURCES,2009, 21(2): 41-44.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2009.02.09     OR     https://www.gtzyyg.com/EN/Y2009/V21/I2/41
[1] Junbin DUAN, Peng PENG, Zhi YANG, Le LIU. Prediction of polymetallic metallogenic favorable area based on ASTER data[J]. Remote Sensing for Land & Resources, 2019, 31(3): 193-200.
[2] Xuewen XING, Song LIU, Degang XU, Kaijun QIAN. Thickness estimation of crude oil slicks by hyperspectral data based on partial least square regression method[J]. Remote Sensing for Land & Resources, 2019, 31(2): 111-117.
[3] Dongyang WU, Li MA. Multi-manifold LE algorithm for dimension reduction and classification of multitemporal hyperspectral image[J]. Remote Sensing for Land & Resources, 2018, 30(2): 80-86.
[4] YANG Yuwei, DAI Xiaoai, NIU Yutian, LIU Hanhu, YANG Xiaoxia, LAN Yan. Inversion of leaf area index in Heihe Oasis based on CASI data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 179-184.
[5] FAN Xue, LIU Qingwang, TAN Bingxiang. Classification of forest species using airborne PHI hyperspectral data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 110-116.
[6] LI Guanghui, WANG Cheng, XI Xiaohuan, ZHENG Zhaojun, LUO Shezhou, YUE Cairong. Extraction of glacier snowline based on airborne LiDAR and hyperspectral data fusion[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 79-84.
[7] ZHANG Yuan-Fei, Wu De-Wen, ZHANG Gen-Zhong, ZHU Gu-Chang, LI Hong. Study on Band Sequence Structure Analysis of Hyperspectral Data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(1): 30-38.
[8] FAN Xue-wei, ZHANG Han-de, SUN Xing-wen. THE APPLICATION OF HYPERSPECTRAL DATA TO THE DETECTION AND IDENTIFICATION OF RED TIDES[J]. REMOTE SENSING FOR LAND & RESOURCES, 2003, 15(1): 8-12.
[9] Liu Dechang, Xie Hongjie, Li Jianfeng, Zhao Yingjun, Huang Shutao, Zhang Jinye, Dong Jishi, Chen Baoshu. HYPERSPECTRAL DATA PROCESSING AND RESEARCH ON GEOLOGICAL APPLICATION IN MIAOERSHAN DISTRICT, GUANGXI PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 1999, 11(3): 65-71.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech