MAP-MRF Approach to Landsat ETM+ SLC-Off Image Classification

Land cover classification is an important application of Landsat images. Unfortunately, the scan-line corrector (SLC) failure in 2003 causes about 22% pixels to remain unscanned in each Landsat 7 ETM+ image. This problem seriously limits the application of Landsat 7 ETM+ images for land cover classification. A common strategy for addressing this problem is filling the unscanned gaps before classification work. However, the simple and high-speed methods for gap-filling cannot yield satisfactory results, especially for heterogeneous landscapes, while the gap-filling methods with high accuracy are often complicated and inefficient in the use of time. This paper develops a new method based on the maximum a posteriori decision rule and Markov random field theory (the MAP-MRF classification framework) for classifying SLC-off ETM+ images without filling unscanned gaps beforehand. The proposed method efficiently avoids the complicated process for gap-filling. The performance of the proposed method was validated by simulated SLC-off images. The results show that the classification accuracy of the proposed method is even higher than that of classification from an image filled by the precise gap-filling algorithm neighborhood similar pixel interpolator, which indicates that an accurate land cover map can be generated without spending time and effort to fill gaps in SLC-off images prior to the land cover classification.