文献类型：期刊文献

英文题名：Ice Identification with Error-Accumulation Enhanced Neural Dynamics in Optical Remote Sensing Images

作者：Xiong, Yizhen[1];Wang, Difeng[2];Fu, Dongyang[1];Huang, Haoen[3]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524025, Peoples R China;[2]Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China;[3]Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430070, Peoples R China

年份：2023

卷号：15

期号：23

外文期刊名：REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001116311500001)、、EI(收录号：20235015199480)、Scopus(收录号：2-s2.0-85179123804)、WOS

基金：No Statement Available

语种：英文

外文关键词：arctic sea ice identification; error-accumulation enhanced neural dynamics (EAEND) model; noise immunity; optical remote sensing image

外文摘要：Arctic sea ice plays an important role in Arctic-related research. Therefore, how to identify Arctic sea ice from remote sensing images with high quality in an unavoidable noise environment is an urgent challenge to be solved. In this paper, a constrained energy minimization (CEM) method is applied for Arctic sea ice identification, which only requires the target spectrum. Moreover, an error-accumulation enhanced neural dynamics (EAEND) model with strong noise immunity and high computing accuracy is proposed to aid with the CEM method for Arctic sea ice identification. With the theoretical analysis, the proposed EAEND model possesses a small steady-state error in noisy environments. Finally, compared with other existing models, the proposed EAEND model can not only complete sea ice identification in excellent fashion, but also has the advantages of high efficiency and noise immunity.