A Cooperative Framework Based on Active and Semi-supervised Learning for Sea Ice Classification using EO-1 Hyperion Data

Abstract

In the classification of remote-sensing sea ice images, labeled samples are difficult to acquire. To adequately utilize the massive number of unlabeled samples, which contain abundant information, we propose a cooperative framework based on active learning (AL) and semi-supervised learning (SSL) for sea ice image classification. We acquire the most valuable samples using AL and make full use of the abundant information contained in the unlabeled samples using SSL, and then conduct a label consistency verification procedure to further ensure the quality of the pseudo-labeled samples obtained through cooperation between AL and SSL. In the AL part, we adopt a sampling strategy that integrates uncertainty and diversity criteria to acquire the most valuable samples to label. In the SSL part, we utilize the SSL sampling strategy to choose the unlabeled samples with the most information and little redundancy, and use the transductive support vector machine (TSVM) as the classification model. The cooperation between AL and SSL ensures the accuracy of the pseudo-labeled samples through a consistency verification procedure. We conduct comparative experiments using the method proposed and other methods on two hyperspectral images obtained from the Earth Observation Satellite 1 (EO-1). The proposed method achieves the highest classification accuracy for both datasets and can be effectively applied to sea ice classification.

Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification

“…However, it is also true that the application of SSL architectures to sea ice classification is very limited. For example, Han et al [51] investigated an approach for sea-ice classification based on active learning (AL) and SSL. They acquired the most informative data examples considering AL.…”

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

“…Experimental comparisons revealed that the SVM-CRF achieved the best performance. Moreover, by utilizing Transductive Support Vector Machines (TSVMs) as the classifier had good performance on two hyperspectral images obtained from EO-1 [73].…”

Fine extraction of arctic sea ice based on CA-DeepLabV3+ model

“…In the past few decades, the extraction of sea ice using remote sensing techniques has become a widely researched topic. Traditional approaches for sea ice identification mostly rely on the distribution characteristics of grayscale information in images and conventional machine learning methods [1][2][3][4][5] . However, these traditional sea ice extraction methods face challenges in terms of accuracy and efficiency.…”