AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (3.5 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Publishing Language: Chinese | Open Access

Extraction of coastal cultivation areas based on Sentinel-2 remote sensing imagery

Yizhou WUDeyong HU( )
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048; Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048
Show Author Information

Abstract

China's coastal aquaculture accounts for a large proportion across the world. Quickly obtaining information on the size and distribution of coastal aquaculture areas is conducive to the monitoring and planning, yield estimation and disaster prevention of aquaculture areas. In view of the difficulties in distinguishing raft aquaculture area from seawater, low recognition accuracy and"salt and pepper"noise in the process of raft aquaculture area extraction, this paper takes the raft aquaculture area near Changshan Islands as the research area, and uses Sentinel-2 satellite remote sensing image data to construct the features of spectrum, texture and geometric. The feature space optimization(FSO) is used to obtain the dominant features of raft aquaculture area extraction. The object-oriented random forest, decision tree and nearest neighbor algorithms are used to extract the raft aquaculture area in the study area. Based on the analysis and comparison of the extraction results. The optimal classification algorithm is summarized, and the reliability of FSO is verified. The results show that: (1) the normalized difference water index, geometric feature area and length, and gray level co-occurrence matrix correlation are the optimal features for identifying raft culture areas. (2)The classification after feature optimization ensures the extraction accuracy, reduces data redundancy, improves the operation efficiency, and has high reliability and applicability for the extraction of raft culture.(3) The classification method based on feature selection and object-oriented random forest have the best comprehensive evaluation. The overall classification accuracy is 88.8% and κ=0.801, this method can effectively avoid the occurrence of"salt and pepper"noise and can extract the thematic information of raft culture area efficiently and accurately. This study can provide technical and thematic data support for dynamic monitoring and yield estimation of raft culture.

CLC number: TP722.4 Document code: A

References

【1】
【1】
 
 
Journal of Capital Normal University (Natural Science Edition)
Pages 11-18

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
WU Y, HU D. Extraction of coastal cultivation areas based on Sentinel-2 remote sensing imagery. Journal of Capital Normal University (Natural Science Edition), 2024, 45(5): 11-18. https://doi.org/10.19789/j.1004-9398.2024.05.002

257

Views

3

Downloads

0

Crossref

Received: 29 October 2022
Published: 01 October 2024
© The editorial department of Journal of Capital Normal University (Natural Science Edition) 2025.

This is an open access article under the CC BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/).