文献类型：期刊文献

英文题名：A Generalized Complex-Valued Constrained Energy Minimization Scheme for the Arctic Sea Ice Extraction Aided With Neural Algorithm

作者：Fu, Dongyang[1];Huang, Haoen[1];Xiao, Xiuchun[1];Xia, Linghui[2];Jin, Long[3]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524025, Peoples R China;[2]China Univ Petr East China, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China;[3]Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China

年份：2022

卷号：60

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:000761235300005)、、EI(收录号：20214911285669)、Scopus(收录号：2-s2.0-85120579680)、WOS

基金：This work was supported in part by the Key Projects of the Guangdong Education Department under Grant 2019KZDXM019, in part by the Chinese Academy of Sciences "Light of West China" Program, in part by the Natural Science Foundation of Chongqing, China, under Grant cstc2020jcyj-zdxmX0028, in part by the Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) under Grant ZJW-2019-08, in part by the High-Level Marine Discipline Team Project of Guangdong Ocean University under Grant 00202600-2009, in part by the First Class Discipline Construction Platform Project in 2019 of Guangdong Ocean University under Grant 231419026, in part by the Guangdong Graduate Academic Forum Project under Grant 230420003, in part by the Postgraduate Education Innovation Project of Guangdong Ocean University under Grant 202159, in part by the Guangdong Graduate Education Innovation Project, Graduate Academic Forum under Grant 2020XSLT27, in part by the Key Laboratory of Digital Signal and Image Processing of Guangdong Province under Grant 2019GDDSIPL-01, and in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515011847.

语种：英文

外文关键词：Sea ice; Arctic; Minimization; Data mining; Remote sensing; Scattering; Robustness; Arctic sea ice extraction; generalized complex-valued constrained energy minimization (GCVCEM) scheme; modified Newton integration (MNI) neural algorithm; noise-tolerance ability

外文摘要：Due to the significant role of sea ice in the Arctic-related research, developing high-precision and robust Arctic sea ice extraction techniques for multi-source remote-sensing images encounters a great challenge. In the light of the constrained energy minimization scheme, this article provides a generalized complex-valued constrained energy minimization (GCVCEM) scheme for the Arctic sea ice extraction with strong robustness and accessible implementation. Given the fact that the image extraction process is easily disturbed by noise in real-life application scenarios, a modified Newton integration (MNI) neural algorithm with the noise-tolerance ability and high extraction accuracy is proposed to aid the GCVCEM scheme. Its key idea is to add an error integration feedback term on the basis of the Newton-Raphson iterative (NRI) algorithm to resist noise perturbation on the solution process of the GCVCEM scheme for high-precision and robust extraction of the Arctic sea ice. Besides, the corresponding convergence analyses and robustness proofs on the proposed MNI neural algorithm are furnished. To evaluate the extraction performance of the proposed MNI neural algorithm, multiple comparative experiments with different sea ice observation images and different noise workspaces are performed. Both the visualized and quantitative experimental results substantiate the superiorities of the proposed MNI neural algorithm aided the GCVCEM scheme for the Arctic sea ice extraction.