Mangrove Extraction from Compact Polarimetric Synthetic Aperture Radar Images Based on Optimal Feature Combinations
文献类型：期刊论文
| 作者 | Shu, Sijing1,2,3,4,5; Yang, Ji2,3; Jing, Wenlong2,3; Yang, Chuanxun2,3; Wu, Jianping2 |
| 刊名 | FORESTS
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 15期号:11页码:22 |
| 关键词 | mangrove identification compact polarimetric SAR polarimetric features image classification Gaofen-3 |
| DOI | 10.3390/f15112047 |
| 英文摘要 | As a polarimetric synthetic aperture radar (SAR) mode capable of simultaneously acquiring abundant surface information and conducting large-width observations, compact polarimetric synthetic aperture radar (CP SAR) holds great promise for mangrove dynamics monitoring. Nevertheless, there have been no studies on mangrove identification using CP SAR. This study aims to explore the potential of C-band CP SAR for mangrove monitoring applications, with the objective of identifying the most effective CP SAR descriptors for mangrove discrimination. A systematic comparison of 52 well-known CP features is provided, utilizing CP SAR data derived from the reconstruction of C-band Gaofen-3 quad-polarimetric data. Among all the features, Shannon entropy (SE), a random polarimetric constituent (VB), Shannon entropy (SEI), and the Bragg backscattering constituent (VG) exhibited the best performance. By combining these four features, we designed three supervised classifiers-support vector machine (SVM), maximum likelihood (ML), and artificial neural network (ANN)-for comparative analysis experiments. The results demonstrated that the optimal polarimetric feature combination not only reduced the redundancy of polarimetric feature data but also enhanced overall accuracy. The highest accuracy of mangrove extraction reached 98.04%. Among the three classifiers, SVM outperformed the other classifiers in mangrove extraction, while ML achieved the highest overall classification accuracy. |
| WOS研究方向 | Forestry |
| 语种 | 英语 |
| WOS记录号 | WOS:001366691400001 |
| 源URL | [http://ir.gig.ac.cn/handle/344008/81989]  |
| 专题 | 中国科学院广州地球化学研究所 |
| 通讯作者 | Yang, Ji |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Guangdong Acad Sci, Southern Marine Sci & Engn Guangdong Lab Guangzhou, Guangdong Prov Key Lab Remote Sensing & Geog Infor, Guangdong Open Lab Geospatial Informat Technol & A, Guangzhou 510070, Peoples R China 3.Southern Marine Sci & Engn Guangdong Lab Guangzhou, Guangzhou 511458, Peoples R China 4.Minist Nat Resources, Technol Innovat Ctr Ocean Telemetry, Qingdao 266061, Peoples R China 5.Chinese Acad Sci, Guangzhou Inst Geochem, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shu, Sijing,Yang, Ji,Jing, Wenlong,et al. Mangrove Extraction from Compact Polarimetric Synthetic Aperture Radar Images Based on Optimal Feature Combinations[J]. FORESTS,2024,15(11):22. |
| APA | Shu, Sijing,Yang, Ji,Jing, Wenlong,Yang, Chuanxun,&Wu, Jianping.(2024).Mangrove Extraction from Compact Polarimetric Synthetic Aperture Radar Images Based on Optimal Feature Combinations.FORESTS,15(11),22. |
| MLA | Shu, Sijing,et al."Mangrove Extraction from Compact Polarimetric Synthetic Aperture Radar Images Based on Optimal Feature Combinations".FORESTS 15.11(2024):22. |
入库方式： OAI收割
来源：广州地球化学研究所
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