Application of geographically weighted regression to fill gaps in SLC-off Landsat ETM+ satellite imagery

Abstract

Landsat 7 enhanced thematic mapper plus (ETM+) satellite imagery is an important data source for many applications. However, the scan line corrector (SLC) failed on 31 May 2003. As a result of the SLC failure, about 22% of the image data is missing in each scene; this is especially pronounced away from nadir. In this article, a local regression method called geographically weighted regression (GWR) is introduced for filling the gaps of the Landsat ETM+ imagery, and it is compared with kriging/cokriging for this purpose. The case studies show that the GWR approach is an effective technique to fill gaps in Landsat ETM+ imagery, although the image restoration is still not perfect. GWR performed marginally better than the complex cokriging method, which too has proven to be an effective method, but is computationally intensive. Although there are visible seam lines at the edges of the filled wide gaps in some bands, the validation results – including RMSE values, error distribution maps, and classification results for the case studies – demonstrate that the DN values estimated by GWR are in fact closer to those of the original image than the corresponding values estimated by kriging/cokriging.