Extraction of Oil Spill Information Using Decision Tree Based Minimum Noise Fraction Transform
Extraction of Oil Spill Information Using Decision Tree Based Minimum Noise Fraction Transform
复制标题
基于决策树的最小噪声分数变换提取溢油信息
DOI：
10.1007/s12524-015-0499-4
复制
发表时间：
2016-01
影响因子：
2.5
通讯作者：
Xueyuan ZHU
中科院分区：
文献类型：
--
作者：
Bingxin LIU;Ying LI;Peng CHEN;Xueyuan ZHU
为了减少高光谱遥感数据处理的波段数，提高处理效率，提出了一种基于最小噪声分数变换的决策树分类方法。利用MNF变换降低数据冗余度，分离图像噪声。通过分析地物的MNF特征值，建立分类决策树，提取油膜相对厚度等信息。结果表明，该方法在保证识别精度的同时，实现了光谱维度信息的高效利用。同时，大大减少了数据处理时间，这在溢油应急响应中非常重要。
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.
登录
查看更多内容
DOI：
--
发表时间：
2007
期刊：
Remote Sensing Information
影响因子：
--
作者：
L. Hai-tao
通讯作者：
L. Hai-tao
影响因子：
20.6
作者：
Solberg, Anne H. Schistad
通讯作者：
Solberg, Anne H. Schistad
影响因子：
8.2
作者：
GREEN, AA;BERMAN, M;CRAIG, MD
通讯作者：
CRAIG, MD
DOI：
10.1117/12.631149
发表时间：
2005-11
期刊：
--
影响因子：
--
作者：
J. Plaza;R. Pérez;A. Plaza;P. Martínez;D. Valencia
通讯作者：
J. Plaza;R. Pérez;A. Plaza;P. Martínez;D. Valencia
影响因子：
2.5
作者：
H. Srivastava;T. Singh
通讯作者：
H. Srivastava;T. Singh