Sea Ice Classification Using Mutually Guided Contexts

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 19
• Main Authors: Sun, Xiaoyu, Zhang, Xi, Huang, Weimin, Han, Zongjun, Lyu, Xinrong, Ren, Peng
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Context; Context feature; Data mining; Feature extraction; Iterative methods; mutually guided contexts; Pixels; Remote sensing; Representations; Sea ice; sea ice classification; small training data; Task analysis; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3268715

In this article, sea ice classification on a remote sensing image given just a small number of labeled pixels is investigated. Effective sea ice classification is rendered from two aspects. First, a feature extraction method is developed. It extracts the context feature from a classification map. Second, an iterative learning paradigm is established. The labeled pixels are divided into two training subsets. At each iteration, the context feature for one subset is extracted from the classification map which is obtained subject to the other subset. Therefore, the two subsets mutually guide each other in updating the context feature in an iterative manner, which finally renders effective sea ice classification. The above-mentioned paradigm is referred to as mutually guided contexts. The advantages of the new paradigm are twofold. First, the context feature enriches the sea ice image representation in a general manner regardless of the types of raw image data. Second, the two training subsets keep providing different refined classification maps for each other such that the comprehensiveness of the context feature is recursively enhanced. Therefore, the paradigm of mutually guided contexts comprehensively characterizes the sea ice image representation for training and classification even when only small training data are available. Experiments validate the effectiveness of the mutually guided contexts for sea ice classification.