In this paper, four widely used temporal compositing algorithms, i.e.median, maximum NDVI, medoid, and weighted scoring-basedalgorithms, were evaluated for annual land cover classification usingmonthly Landsat time se...In this paper, four widely used temporal compositing algorithms, i.e.median, maximum NDVI, medoid, and weighted scoring-basedalgorithms, were evaluated for annual land cover classification usingmonthly Landsat time series data. Four study areas located in California,Texas, Kansas, and Minnesota, USA were selected for image compositingand land cover classification. Results indicated that images compositedusing weighted scoring-based algorithms have the best spatial fidelitycompared to other three algorithms. In addition, the weighted scoringbasedalgorithms have superior classification accuracy, followed bymedian, maximum NDVI, and medoid in descending order. However, themedian algorithm has a significant advantage in computational efficiencywhich was ~70 times that of weighted scoring-based algorithms, andwith overall classification accuracy just slightly lower (~0.13% onaverage) than weighted scoring-based algorithms. Therefore, werecommended the weighted scoring-based compositing algorithms forsmall area land cover mapping, and median compositing algorithm forthe land cover mapping of large area considering the balance betweencomputational complexity and classification accuracy. The findings of thisstudy provide insights into the performance difference between variouscompositing algorithms, and have potential uses for the selection ofpixel-based image compositing technique adopted for land covermapping based on Landsat time series data.展开更多
基金supported by the National Natural Science Foundation of China[grant number 42271412].
文摘In this paper, four widely used temporal compositing algorithms, i.e.median, maximum NDVI, medoid, and weighted scoring-basedalgorithms, were evaluated for annual land cover classification usingmonthly Landsat time series data. Four study areas located in California,Texas, Kansas, and Minnesota, USA were selected for image compositingand land cover classification. Results indicated that images compositedusing weighted scoring-based algorithms have the best spatial fidelitycompared to other three algorithms. In addition, the weighted scoringbasedalgorithms have superior classification accuracy, followed bymedian, maximum NDVI, and medoid in descending order. However, themedian algorithm has a significant advantage in computational efficiencywhich was ~70 times that of weighted scoring-based algorithms, andwith overall classification accuracy just slightly lower (~0.13% onaverage) than weighted scoring-based algorithms. Therefore, werecommended the weighted scoring-based compositing algorithms forsmall area land cover mapping, and median compositing algorithm forthe land cover mapping of large area considering the balance betweencomputational complexity and classification accuracy. The findings of thisstudy provide insights into the performance difference between variouscompositing algorithms, and have potential uses for the selection ofpixel-based image compositing technique adopted for land covermapping based on Landsat time series data.
