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Sea Ice Classification Using Mutually Guided Contexts
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AI
DOI：10.1109/TGRS.2023.3268715.png)
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Abstract
En 中文
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.
Keywords:
Sea ice
Feature extraction
Remote sensing
Training
Task analysis
Iterative methods
Data mining
Context feature
mutually guided contexts
sea ice classification
small training data
Journal
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2.1W
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10.7W