%A Xuewen XING, Song LIU, Degang XU, Kaijun QIAN %T Thickness estimation of crude oil slicks by hyperspectral data based on partial least square regression method %0 Journal Article %D 2019 %J Remote Sensing for Natural Resources %R 10.6046/gtzyyg.2019.02.16 %P 111-117 %V 31 %N 2 %U {https://www.gtzyyg.com/CN/abstract/article_7238.shtml} %8 2019-06-15 %X

Thickness of oil slick is an important parameter of oil spill volume. In order to confirm the feasibility of oil thickness estimation with hyperspectral data,the authors used ASD FieldSpec3, quartz halogen lamp and crude oil for a laboratory experiment which simulates oil slick and spectral measurement. 27 pairs of oil thickness and reflection data were acquired. To make full use of spectral information of the hyperspectral data,the authors selected partial least square (PLS) to slick thickness and reflection modeling with 21 set model data and 6 test data set. Model result shows that PLS model expresses optimal effect when five principal components are selected which interpret 74% information of independent variables and 99.8% information of dependent variable, the prediction capability of the model runs up to 92.8%. The root mean squared error is 0.01 for modeling samples and 0.04 for validation samples. The PLS model shows better accuracy of modeling and validation error compared with traditional model, and thus it can be used in oil slicks thickness modeling with hyperspectral data.