A Transfer-Learning-Like Neural Dynamics Algorithm for Arctic Sea Ice Extraction

Abstract

Sea ice plays a pivotal role in ocean-related research, necessitating the development of highly accurate and robust techniques for its extraction from diverse satellite remote sensing imagery. However, conventional learning methods face limitations due to the soaring cost and time associated with manually collecting sufficient sea ice data for model training. This paper introduces an innovative approach where Neural Dynamics (ND) algorithms are seamlessly integrated with a recurrent neural network, resulting in a Transfer-Learning-Like Neural Dynamics (TLLND) algorithm specifically tailored for sea ice extraction. Firstly, given the susceptibility of the image extraction process to noise in practical scenarios, an ND algorithm with noise tolerance and high extraction accuracy is proposed to address this challenge. Secondly, The internal coefficients of the ND algorithm are determined using a parametric method. Subsequently, the ND algorithm is formulated as a decoupled dynamical system. This enables the coefficients trained on a linear equation problem dataset to be directly generalized to solve the sea ice extraction challenges. Theoretical analysis ensures that the effectiveness of the proposed TLLND algorithm remains unaffected by the specific characteristics of various dataset. To validate its efficacy, robustness, and generalization performance, several comparative experiments are conducted using diverse Arctic sea ice satellite imagery with varying levels of noise. The outcomes of these experiments affirm the competence of the proposed TLLND algorithm in addressing the complexities associated with sea ice extraction.