Evaluation of segment-based gap-filled Landsat ETM plus SLC-off satellite data for land cover classification in southern Saskatchewan, Canada

This paper describes single-date and multi-date land-cover classification accuracy results using segment-based, gap-filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single-date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap-filled Landsat data. Area estimates for the mid-summer images differed, on average, from 0.6% to 1.9% for a four-class and eight-class classification, respectively. A multi-date land-cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%.