Citation

Saberi, Zamfirdaus and Hashim, Noramiza and Ali, Aziah and Abdullah, Junaidi and Mohd Isa, Wan Noorshahida and Che Embi, Zarina (2022) Multi-temporal satellite image correction based on CNN features and adaptive contrast enhancement. IOP Conference Series: Earth and Environmental Science, 1064 (1). 012019. ISSN 1755-1307

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Abstract

In recent years, identifying changes in multi-temporal images in terms of land use and land cover has been significant in a variety of applications, including urban planning. Due to weather and environmental effects, optical remote sensing has limitations in obtaining images where the image quality may be degraded. It's because the images being registered are taken at various times, viewpoints, and types of sensors. In this article, the pre-processing methods, which include radiometric correction and geometric correction, are introduced to enhance the quality of satellite images and identify correct spatial alignment. For radiometric correction, adaptive contrast enhancement is done by combining histogram- and non-linear transfer function-based approaches in CIELAB color space. A comparison study is done to see how the new method compared to other methods. For geometric correction, the features from two images are extracted using Convolutional Neural Network to match and align them. The introduced approach for radiometric correction gave the best average rank of BRISQUE scores and RMSE of contrast scores, and the geometric correction can align two images together with an average accuracy of improvement of 91.78 percent. The findings of this research will provide the preliminary step for any change detection activities.

Item Type:	Article
Uncontrolled Keywords:	Remote sensing, Convolutional neural network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	14 Sep 2022 01:25
Last Modified:	14 Sep 2022 01:25
URII:	http://shdl.mmu.edu.my/id/eprint/10433

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