Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method

Abstract Since the scan line corrector (SLC) of the Landsat Enhanced Thematic Mapper Plus (ETM +) sensor failed permanently in 2003, about 22% of the pixels in an SLC-off image are not scanned. To improve the usability of the ETM + SLC-off data, we propose an integrated method to recover the missing pixels. The majority of the degraded pixels are filled using multi-temporal images as referable information by building a regression model between the corresponding pixels. When the auxiliary multi-temporal data cannot completely recover the missing pixels, a non-reference regularization algorithm is used to implement the pixel filling. To assess the efficacy of the proposed method, simulated and actual SLC-off ETM + images were tested. The quantitative evaluations suggest that the proposed method can predict the missing values very accurately. The method performs especially well in edges, and is able to keep the shape of ground features. According to the assessment results of the land-cover classification and NDVI, the recovered data are also suitable for use in further remote sensing applications.

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