2025年 06期

Wetland Classification Method in Dongying City Based on Combination Optimization and Random Forest Algorithm


摘要(Abstract):

为了有效解决在融合多种类特征因子及多时相遥感数据进行湿地分类时出现的数据冗余、精度下降问题,同时考虑多时相遥感影像在不同数量和组合时分类精度存在差异的现状,基于谷歌地球引擎(GEE)遥感云平台提出一种融合特征因子优选和时相组合优选的分类方法,利用随机森林算法在32类特征因子中筛选出对湿地分类具有显著贡献的18类因子,并分析多时相影像数量及组合方式对分类精度的影响,最终优选出适合山东省东营市湿地信息提取的分类方案。结果表明:特征优选能精选出对湿地分类具有显著贡献的特征因子,在确保输入足够特征种类的同时减少特征输入个数,降低数据冗余,提升分类精度;不同特征因子对湿地分类贡献度不同,其中光谱特征、水体指数、纹理特征、建筑指数对湿地分类贡献度较高;融合多时相遥感影像有利于提升分类精度,但随着影像数量的持续加入,分类精度提升幅度逐渐减小,兼顾影像质量及数量是提升分类精度的有效手段;结合特征优选与时相优选对东营市进行的5期湿地分类的总体分类精度均在85%以上,Kappa系数均在0.75以上,表明东营市人工湿地面积呈增大趋势,自然湿地面积呈减小趋势。

关键词(KeyWords):湿地;遥感影像;特征优选;时相优选;谷歌地球引擎;东营市

基金项目(Foundation):国家自然科学基金项目(42272288);; 山东省自然科学基金项目(ZR2019MD029);; 山东省高校院所创新团队项目(2021GXRC070)

作者(Author):刘中业,侯金霄,纪汶龙,黄林显,邢立亭,朱恒华,韩忠

DOI:10.13349/j.cnki.jdxbn.20251011.001

参考文献(References):

[1] SU H J,YAO W J,WU Z Y,et al.Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2021,171:238.

[2] WANG W L,SUN M Z,LI Y,et al.Multi-level comprehensive assessment of constructed wetland ecosystem health:a case study of Cuihu Wetland in Beijing,China[J].Sustainability,2022,14(20):13439.

[3] 何振芳,牟婷婷,郭庆春,等.1979—2019年大汶河流域湿地时空演变与分异研究[J].水资源保护,2024,40(2):134.

[4] WU B B,WANG G Q,WANG Z G,et al.Integrated hydrologic and hydrodynamic modeling to assess water exchange in a data-scarce reservoir[J].Journal of Hydrology,2017,555:15.

[5] 徐振田,ALI S,张莎,等.基于Landsat数据的黄河三角洲湿地提取及近30年动态研究[J].海洋湖沼通报,2020,42(3):70.

[6] 解淑毓,付波霖,李颖,等.基于多维度遥感影像的洪河国家级自然保护区沼泽湿地分类方法研究[J].湿地科学,2021,19(1):1.

[7] HAO B F,MA M G,LI S W,et al.Land use change and climate variation in the three gorges reservoir catchment from 2000 to 2015 based on the Google Earth Engine[J].Sensors,2019,19(9):2118.

[8] 黄立贤,沈志学.高光谱遥感图像的监督分类[J].地理空间信息,2011,9(5):81.

[9] HONG Y,LI D R,WANG M,et al.Cotton cultivated area extraction based on multi-feature combination and CSSDI under spatial constraint[J].Remote Sensing,2022,14(6):1392.

[10] DU B J,MAO D H,WANG Z M,et al.Mapping wetland plant communities using unmanned aerial vehicle hyperspectral imagery by comparing object/pixel-based classifications combining multiple machine-learning algorithms[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2021,14:8249.

[11] 侯蒙京,殷建鹏,葛静,等.基于随机森林的高寒湿地地区土地覆盖遥感分类方法[J].农业机械学报,2020,51(7):220.

[12] HU Y B,ZHANG J,MA Y,et al.Hyperspectral coastal wetland classification based on a multiobject convolutional neural network model and decision fusion[J].IEEE Geoscience and Remote Sensing Letters,2019,16(7):1110.

[13] REZAEE M,MAHDIANPARI M,ZHANG Y,et al.Deep con-volutional neural network for complex wetland classification using optical remote sensing imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(9):3030.

[14] 黄佩,普军伟,赵巧巧,等.植被遥感信息提取方法研究进展及发展趋势[J].自然资源遥感,2022,34(2):10.

[15] 高瑞,王志勇,周晓东,等.利用多时相遥感监测与分析黄河三角洲湿地变化动态[J].测绘通报,2021(4):22.

[16] HLADIK C,SCHALLES J,ALBER M.Salt marsh elevation and habitat mapping using hyperspectral and LIDAR data[J].Remote Sensing of Environment,2013,139:318.

[17] 张磊,宫兆宁,王启为,等.Sentinel-2影像多特征优选的黄河三角洲湿地信息提取[J].遥感学报,2019,23(2):313.

[18] 宗秀影,刘高焕,乔玉良,等.黄河三角洲湿地景观格局动态变化分析[J].地球信息科学学报,2009,11(1):91.

[19] BREIMAN L.Random forests.[J].Machine Learning,2001,45(1):5.

[20] HASAN M,NASSER M,AHMAD S,et al.Feature selection for intrusion detection using random forest[J].Journal of Information Security,2016,7(3):129.

[21] SOKOLOVA M,LAPALME G.A systematic analysis of performance measures for classification tasks[J].Information Processing and Management,2009,45(4):427.

[22] REED F J.Homogeneity of Kappa statistics in multiple samples[J].Computer Methods and Programs in Biomedicine,2000,63(1):43.

[23] DU H S,WANG J F,HAN C.High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms[J].Open Geosciences,2022,14(1):228.

[24] 霍轩琳,牛振国,张波,等.高寒湿地分类的遥感特征优选研究[J].遥感学报,2023,27(4):1045.

[25] PATHAK B,BAROOAH D.Texture analysis based on the gray-level co-occurrence matrix considering possible orientations[J].International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering,2013,2(9):4206.

[26] 付扬军,师学义.基于小流域尺度的县域国土空间生态修复分区:以山西汾河上游为例[J].自然资源学报,2023,38(5):1225.

[27] 冯倩,张佳华,邓帆,等.基于特征优选和时空融合算法的黄河三角洲湿地类别制图方法研究[J].自然资源遥感,2024,36(2):39.