Journal
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
Volume 44, Issue 3, Pages 421-426Publisher
SPRINGER
DOI: 10.1007/s12524-015-0499-4
Keywords
Hyperspectral remote sensing; Oil spill monitoring; Minimum noise fraction
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Funding
- Fundamental Research Funds for the Central Universities [3132015006]
- National Natural Science Foundation of China [51509030]
- Natural Science Foundation of Liaoning Province [201502886]
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In order to reduce the number of bands for processing hyperspectral remote sensing data and to improve the processing efficiency, this article proposed a decision tree classification method based on minimum noise fraction (MNF) transform. MNF transform was used to reduce data redundancy, and the image noise was separated. By analyzing the MNF eigenvalues of the ground objects, the classification decision tree was established, and the information such as the relative thickness of the oil film was extracted. The results show that the method can ensure recognition accuracy, and achieve the efficient use of information of spectral dimension. Meanwhile, the data processing time is significantly reduced, which is very important during emergency response to oil spills.
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