| 基于GA-BP神经网络的临洪河口湿地土地覆盖分类算法研究 |
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| 引用本文： | 何爽,卢霞,张森,李珊,唐海童,郑薇,林辉,罗庆龄.基于GA-BP神经网络的临洪河口湿地土地覆盖分类算法研究[J].海洋科学,2020,44(12):44-53. |
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| 作者姓名： | 何爽 卢霞 张森 李珊 唐海童 郑薇 林辉 罗庆龄 |
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| 作者单位： | 江苏海洋大学海洋技术与测绘学院,江苏连云港222005;江苏海洋大学海洋技术与测绘学院,江苏连云港222005;江苏海洋大学海洋技术与测绘学院,江苏连云港222005;江苏海洋大学海洋技术与测绘学院,江苏连云港222005;江苏海洋大学海洋技术与测绘学院,江苏连云港222005;江苏海洋大学海洋技术与测绘学院,江苏连云港222005;江苏海洋大学海洋技术与测绘学院,江苏连云港222005;江苏海洋大学海洋技术与测绘学院,江苏连云港222005 |
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| 基金项目： | 国家自然科学基金（41506106）;2019年连云港市“海燕计划”项目;江苏高校优势学科建设工程资助项目（PAPD）;江苏省海洋技术一流专业建设项目;2019年江苏省研究生实践创新项目（SJCX19_0962）;2020年江苏省研究生实践创新项目 |
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| 摘 要： | 针对传统分类方法易受到"同物异谱"和"同谱异物"影响,致使河口湿地覆盖分类精度较低的问题,提出一种基于遗传算法优化BP神经网络分类算法。以江苏省临洪河口湿地为研究区,选用哨兵Sentinel-2影像,经辐射校正、大气校正和图像裁剪等预处理后,构建基于自适应遗传算法优化的BP神经网络算法开展临洪河口湿地土地覆盖分类研究,并与传统BP神经网络、支持向量机和随机森林算法进行精度比较。研究结果表明：遗传算法优化后的BP神经网络算法开展河口湿地土地覆盖分类的总精度为96.162 7%,Kappa系数为0.952 0;与传统BP神经网络、支持向量机和随机森林分类算法的分类总精度相比,分别提高了7.359 7%、11.677 9%和6.042 4%;对应的Kappa系数也相应提高了0.090 8、0.118 0和0.074 8;有效解决了河口湿地土地覆盖分类精度低的问题。遗传算法优化后的BP神经网络可实现河口湿地土地覆盖的高精度分类,促进湿地资源的合理开发和保护,为实现海洋生态文明建设提供技术支撑。
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| 关 键 词： | 河口湿地 Sentinel-2 土地覆盖分类 遗传算法 神经网络 |
| 收稿时间： | 2020/8/14 0:00:00 |
| 修稿时间： | 2020/9/25 0:00:00 |
Research on classification algorithm of wetland land cover in the Linhong Estuary, Jiangsu Province |
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| HE Shuang,LU Xi,ZHANG Sen,LI Shan,TANG Hai-tong,ZHENG Wei,LIN Hui,LUO Qing-ling.Research on classification algorithm of wetland land cover in the Linhong Estuary, Jiangsu Province[J].Marine Sciences,2020,44(12):44-53. |
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| Authors: | HE Shuang LU Xi ZHANG Sen LI Shan TANG Hai-tong ZHENG Wei LIN Hui LUO Qing-ling |
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| Institution: | School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China |
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| Abstract: | Aiming at the problem that traditional classification methods are susceptible to "same matter with different spectrum" and "same spectrum with foreign matter", resulting in low classification accuracy of estuary wetland coverage, a BP neural network classification algorithm optimized based on genetic algorithm is proposed. The Linhong Estuary wetland in Jiangsu Province was taken as the research area. The Sentinel-2 remote sensing image were chosen and was preprocessed by the radiometric correction, atmospheric correction and image cropping. Due to this, a BP neural network algorithm optimized by adaptive genetic algorithm was conducted to develop the Linhong Estuary wetland land cover classification research. The comparison was performed through classification accuracy among BP neural network algorithm optimized by adaptive genetic algorithm, traditional BP neural network, support vector machine and random forest algorithm. The research results indicated that the total accuracy of BP neural network algorithm optimized by genetic algorithm for estuary wetland land cover classification is 96.162 7%, and the Kappa coefficient is 0.952 0. It was higher than that of the total classification accuracy of traditional BP neural network, support vector machine and random forest classification algorithm. The total accuracy of BP neural network algorithm optimized with adaptive genetic algorithm, traditional BP neural network, support vector machine and random forest algorithm has increased by 7.359 7%, 11.677 9%, and 6.042 4%, respectively. The corresponding Kappa coefficient has also been increased by 0.090 8, 0.118 0, and 0.074 8, respectively. The problem of low accuracy of estuary wetland land cover classification is effectively solved. The BP neural network optimized by genetic algorithm can realize high-precision classification of estuary wetland land cover, promote the rational development and environmental protection of wetland resources, and provide technical support for the construction of marine ecological civilization. |
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| Keywords: | estuary wetland Sentinel-2 land cover classification genetic algorithm neural network |
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