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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
何爽1, 卢霞1, 李珊1, 唐海童2, 郑薇1, 张森1, 林辉1, 罗庆龄1
1.江苏海洋大学;2.江苏海洋大学海洋技术与测绘学院
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96.1627%,Kappa系数为0.9520;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7.3597%、11.6779%和6.0424%;对应的Kappa系数也相应提高了0.0908、0.1180和0.0748;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。
关键词:  河口湿地  Sentinel-2  土地覆盖分类  遗传算法  神经网络
DOI:10.11759/hykx20200814002
分类号:TP 751
基金项目:海滨湿地盐沼植被盐分胁迫高光谱遥感响应机理研究
Research on Classification Algorithm of Wetland Land Cover in Linhong Estuary, Jiangsu Province
HeShuang,LU Xia,LI Shan,TANG Hai-Tong,ZHENG Wei,ZHANG Sen,LIN Hui,LUO Qing-ling
Jiangsu Ocean University
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. Based on this, a BP neural network algorithm optimized based on adaptive genetic algorithm was conducted in order to develop the Linhong estuary wetland land Cover classification research. The comparison was performed trough classification accuracy among BP neural network algorithm optimized based on 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 96.1627%, and the Kappa coefficient is 0.9520. 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 based on adaptive genetic algorithm, traditional BP neural network, support vector machine and random forest algorithm has increased by 7.3597%, 11.6779%, and 6.0424%, respectively. The corresponding Kappa coefficient has also been increased by 0.0908, 0.1180, and 0.0748, 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.
Key words:  Estuary wetland  Sentinel-2  land cover classification  genetic algorithm  neural network
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