Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on the mutual information theory. Subsequently, the second original band with the least similarity is chosen by the spectral correlation measure method. Finally, the subsequent bands are selected through the linear prediction method, and the support vector machine classifier model is utilized for sea ice classification. In the experiments of Baffin Bay, comparative analyses are carried out between the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieves the highest classification accuracy of 91.1805% in the experiment. The experimental results indicate that the ISMLP method exhibits better performance overall than other methods and can be effectively applied in hyperspectral sea ice detection.