Ulva prolifera subpixel mapping with multiple-feature decision fusion  

作　　者：Jianhua WAN Xianci WAN Lie SUN Mingming XU Hui SHENG Shanwei LIU Bin ZOU Qimao WANG 

机构地区：[1]College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580,China [2]Qingdao Ecological and Environment Monitoring Center of Shandong Province,Qingdao 266003,China [3]National Satellite Ocean Application Service,Beijing 100081,China [4]Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural Resources(MNR),Beijing 100081,China

出　　处：《Journal of Oceanology and Limnology》2023年第3期865-880,共16页海洋湖沼学报（英文）

基　　金：Supported by the Shandong Provincial Natural Science Foundation of China(No.ZR2019MD023);the National Natural Science Foundation of China(No.41776182)。

摘　　要：The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U.prolifera patches,omission of tiny patches,and overestimation of coverage area.The decomposition of U.prolifera mixed pixel addresses the issue of coverage area overestimation,and the remaining problems can be alleviated by subpixel mapping(SPM).Due to the drift and dissipation of U.prolifera,a suitable SPM method is the single image-based unsupervised method.However,the method has difficulties in detail reconstruction,insufficient learning of spectral information,and SPM error introduced by abundance deviation.Therefore,we proposed a multiple-feature decision fusion SPM(MFDFSPM)method.It involves three branches to obtain the spatial,abundance,and spectral features of U.prolifera while considers multi-feature information using the fusion strategy.Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical U.prolifera SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation,which produced subpixel map with more detailed spatial information and less noise.

关 键 词：Ulva prolifera subpixel mapping multiple-feature decision fusion abundance geostationary ocean color imager(GOCI) 

分 类 号：X87[环境科学与工程—环境工程] X834