Name
Abstract | ||
|---|---|---|
Long-term Landsat time series imagery provides valuable opportunities for monitoring land surface changes. However, missing observations that result from clouds, cloud shadows, and scan line corrector failure makes Landsat data record neither a continuous nor a consistent time series. In this study, we present an approach to produce a gap-free Landsat time series for the Taita Hills in southeastern Kenya. We used simulated gaps in nearly cloud-free images to assess the performance of the approach while considering two factors: the size of the gaps and effect of the wet or dry seasons. It turned out that filling image with the largest area of simulated gaps, which were equivalent to 52% of the test image area, yielded almost the same root mean square error, relative RMSE and the coefficient of determination as the smallest gaps equivalent to 25%. Furthermore, gap-free images in both dry and wet seasons could be filled without visual artifacts, and dry season images were better predicted. Finally, the time series images produced for the study area showed consistent temporal variation across land use/land cover types. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9324671 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
Tropical areas, image processing, image reconstruction, gap-filling | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhipeng Tang | 1 | 0 | 0.34 |
| Hari Adhikari | 2 | 0 | 0.34 |
| Petri K. E. Pellikka | 3 | 0 | 0.34 |
| Janne Heiskanen | 4 | 0 | 0.34 |