NDLI: Automatic Geometric Correction of Complex Sea Condition Remote Sensing Image Based on Decision Tree Classification

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Automatic Geometric Correction of Complex Sea Condition Remote Sensing Image Based on Decision Tree Classification

Content Provider	Scilit
Author	Hua, Li Sui, Haigang Ding, Wei Fu, Hongbo
Copyright Year	2019
Description	Journal: Iop Conference Series: Earth and Environmental Science The geometric correction of ocean remote sensing image is a prerequisite for its data application. In this paper, to solve the problem that the sea island is sparse, cloud interference is severe, the control point is difficult to obtain, an automatic correction technique based on decision tree classification is proposed. In this paper, the image is processed by the method of super-pixel segmentation first. Then, the spectral and texture features in the superpixels are selected, including the energy value, the entropy and the correlation value of the gray level co-occurrence matrix and the normalized water index. Finally, the tree image classification model is used to classify the image superpixels, and the clear sky area which will be matched directly with the reference image can be extracted. Through the template matching and polynomial geometric model, the geometric correction of the remote sensing image is automatically corrected. Through the experiment of Landset8 OLI_TIRS image, compared with the classification results of the other two classification methods, the final precision is better than the other two methods. Therefore, the technical process proposed in this paper can be applied to the geometric correction of complex sea condition remote sensing images.
Related Links	https://iopscience.iop.org/article/10.1088/1755-1315/326/1/012006/pdf
ISSN	17551307
e-ISSN	17551315
DOI	10.1088/1755-1315/326/1/012006
Journal	Iop Conference Series: Earth and Environmental Science
Issue Number	1
Volume Number	326
Language	English
Publisher	IOP Publishing
Publisher Date	2019-09-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Earth and Environmental Science
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Physics and Astronomy Environmental Science

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