| 融合地理空间认知的珊瑚礁地貌单元高分遥感分类方法 |
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| 引用本文： | 张飞飞,任广波,胡亚斌,马毅.融合地理空间认知的珊瑚礁地貌单元高分遥感分类方法[J].海洋技术,2023(1):1-15. |
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| 作者姓名： | 张飞飞 任广波 胡亚斌 马毅 |
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| 作者单位： | 1. 内蒙古师范大学地理科学学院;2. 自然资源部第一海洋研究所;3. 自然资源部海洋遥测技术创新中心 |
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| 基金项目： | 国家重点研发计划资助项目 （2022YFC3105100）；中国高分辨率对地观测专项资助项目 （41-Y30F07-9001-20/22）；海洋领域融合应用示范项目 （RHYJKF02）；国家自然科学基金重点资助项目 （51839002） |
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| 摘 要： | 近年来，在人为活动和自然因素的影响下，全球珊瑚礁面临着大规模退化问题，开展珊瑚礁监测研究对珊瑚礁生态系统评估、修复和保护工作具有重要作用。本文以西沙群岛北礁和华光礁为研究区，应用2015年高分二号（GF-2）和WorldView-2高空间分辨率卫星影像和现场调查数据，基于不同珊瑚礁地貌单元的空间位置特征，提出了融合地理空间认知（Geo-Spatial Cognition,GSC）的珊瑚礁地貌单元高分遥感分类方法。研究结果表明：针对因空间位置不同和底质组成高度近似导致珊瑚礁地貌单元漏分和错分的问题，本文提出的方法更能有效获取精准的珊瑚礁地貌单元信息。其中，融合地理空间认知的随机森林（Integrating Geo-Spatial Cognition-Random Forest,GSC-RF）方法展现出了最优的分类表现，在北礁和华光礁珊瑚礁地貌单元分类中总体精度分别为98.06%和91.93%,Kappa系数分别为0.98和0.91。相比于仅使用光谱信息的随机森林（Random Forest,RF）、多元逻辑回归（Multinomial Logistic Regression,MLR）和支...
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| 关 键 词： | 珊瑚礁地貌单元 高分遥感分类 地理空间认知 北礁 华光礁 |
A High-resolution Remote Sensing Classification Method of Coral Reef Geomorphic Units Integrating Geospatial Cognition |
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| Abstract: | In recent years, coral reefs around the world have faced massive degradation due to anthropogenic and natural factors, and
coral reef monitoring studies play an important role in coral reef ecosystem assessment, restoration and conservation. In this paper, Bei Reef
and Huaguang Reef of Xisha Islands are taken as the study areas and Gaofen-2(GF-2)and WorldView-2 high spatial resolution images
and field survey data in 2015 are applied. Based on spatial location characteristics of different coral reef geomorphic units, a high -
resolution remote sensing classification method of integrating Geo-Spatial Cognition(GSC) is proposed. The results of classification showed: Aiming at the problems of omission and misclassification caused by different spatial positions and highly similar sediment composition, the
proposed classification method is more effective in obtaining accurate information on coral reef geomorphic units. Integrating Geo-Spatial
Cognition-Random Forest(GSC-RF) method showed the best classification performance, with an overall accuracy of 98.06% and 91.93%
in Bei Reef and Huaguang Reef, respectively, and the Kappa coefficient is 0.98 and 0.91, respectively. Compared with the classical
classification methods of Random Forest(RF), Multinomial Logistic Regression(MLR) and Support Vector Machine(SVM), which only
use spectral information. The proposed method improves the overall classification accuracy of the Bei Reef and Huaguang Reef by 14%-
25% and 6%-15%, respectively. The new method can significantly improve the classification accuracy of coral reef geomorphic units and
provide technical support for fine monitoring of coral reefs on a large scale. |
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