Ice Identification with Error-Accumulation Enhanced Neural Dynamics in Optical Remote Sensing Images

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...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Yizhen Xiong, Difeng Wang, Dongyang Fu, Haoen Huang
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2023
Subjects:
arctic sea ice identification
error-accumulation enhanced neural dynamics (EAEND) model
noise immunity
optical remote sensing image
Science
Q
Arctic
Sea ice
Online Access:https://doi.org/10.3390/rs15235555
https://doaj.org/article/86a418fdc4fe4594a992082a138d4ad8
Description
Summary: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.

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