Improving Long-term Quality and Continuity of Landsat-7 Data Through Inpainting of Lost Data Based on the Nonconvex Model of Dynamic Dictionary Learning

On May 31, 2003, the scan line corrector (SLC) of the Enhance Thematic Mapper Plus (ETM+) on board the Landsat-7 satellite was broken down, resulting in strips of lost data in the Landsat-7 images, which seriously affected the quality and continuous applications of the ETM+ data for space and earth science. This paper proposes a new inpainting method for repairing the Landsat-7 ETM+ images taking into account the physical characteristics and geometric features of the ground area of which the data are missed. Firstly, the two geometric slopes of the boundaries of each missing stripe of the georeferenced ETM+ image is calculated by the Hough, ignoring the slope of the part of the missing strip that are on the same edges of the whole image. Secondly, an adaptive dictionary was developed and trained using a large number of Landsat-7 ETM+ SLC-ON images. When the adaptive dictionary is used to restore an image with missing data, the dictionary is actually dynamic. Then the data-missing strips were repaired along their slope directions by using the logdet (.) low-rank non-convex model along with dynamic dictionary. Imperfect points are defined as the pixels whose values are quite different from its surrounding pixel values. They can be real values but most likely can be noise. Lastly, the imperfect points after the second step were replaced by using the method of sparse restoration of the overlapping groups. We take the Landsat ETM+ images of June 10, 2002 as the test image for our algorithm evaluation. There is no data missing in this image. Therefore we extract the same missing -stripes of the images of the same WRS path and WRS row as the 2002 image but acquired after 2003 to form the missing-stripe model. Then we overlay the missing-stripe model over the image of 2002 to get the simulated missing image. Fig.1(a)-(c) show the simulated missing images of Bands 1, 3, and 5 of the 2002 ETM+ image data. We apply the algorithm to restore the missing stripes. Fig.1(d)-(f) show the restored images of Bands 1, 3, and 5, corresponding to the images (a)-(c). The repaired images are then compared with the original images band by band and it is found the algorithm works very well. We will show application of the algorithm to other images and the details in comparison.