Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TGRS.2013.2247612 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
geophysical techniques,remote sensing,map-mrf approach,classification,land cover,markov random field theory,land cover map,landsat etm+ slc-off image classification,land cover classification,markov random field (mrf),image classification,geophysical image processing,landsat image application,landsat etm+,heterogeneous landscapes,pixel interpolator,scan-line corrector (slc)-off,classifying slc-off etm+ images | Decision rule,Computer vision,Pattern recognition,Markov random field,Remote sensing,Artificial intelligence,Pixel,Maximum a posteriori estimation,Contextual image classification,Land cover,Mathematics | Journal |
Volume | Issue | ISSN |
52 | 2 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolin Zhu | 1 | 9 | 1.32 |
| Desheng Liu | 2 | 24 | 4.82 |