Reconstruction the Missing Pixels for Landsat ETM+SLC-off Images Using Multiple Linear Regression Model

Abdul Jabar, Asmaa and Sulong, Ghazali and George, Loay and Shafry, Mohd and Abduljabar, Zinah (2016) Reconstruction the Missing Pixels for Landsat ETM+SLC-off Images Using Multiple Linear Regression Model. ARO-The Scientific Journal of Koya University, 4 (2). pp. 15-24. ISSN 24109355

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Abstract

On 31 May 2003, the scan line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor which compensates for the forward motion of the satellite in the imagery acquired failed permanently, resulting in loss of the ability to scan about 20% of the pixels in each Landsat 7 SLC-off image. This permanent failure has seriously hampered the scientific applications of ETM+ images. In this study, an innovative gap filling approach has been introduced to recover the missing pixels in the SLC-off images using multi-temporal ETM+ SLC-off auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two fill images in parallel using the multiple linear regressions (MLR) model. Simulated and actual SLC-off ETM+ images were used to assess the performance of the proposed method by comparing with multi-temporal data based methods, the LLHM method which is based on simple linear regression (SLR) model. The qualitative and quantitative evaluations indicate that the proposed method can recover the value of un-scanned pixels accurately, especially in heterogeneous landscape and even with more temporally distant fill images.

Item Type:	Article
Uncontrolled Keywords:	ETM+SLC-off images, gap filling, multiple Linear Regression (MLR), simple linear regression (SLR).
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	ARO-The Scientific Journal of Koya University > VOL 4, NO 2 (2016)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	03 Aug 2017 20:10
Last Modified:	03 Aug 2017 20:10
URI:	http://eprints.koyauniversity.org/id/eprint/99

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