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Sensors 2017, 17(5), 1124; doi:10.3390/s17051124

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

College of Information Technology, Shanghai Ocean University; Shanghai 201306, China
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Academic Editor: Farid Melgani
Received: 19 February 2017 / Revised: 5 May 2017 / Accepted: 9 May 2017 / Published: 15 May 2017
(This article belongs to the Special Issue Marine Sensing)
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Abstract

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 mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. View Full-Text
Keywords: sea ice; similarity measure; band selection; classification; hyperspectral image sea ice; similarity measure; band selection; classification; hyperspectral image
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Han, Y.; Li, J.; Zhang, Y.; Hong, Z.; Wang, J. Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data. Sensors 2017, 17, 1124.

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