Título
Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Sensors
Datos de la obra
Kwan, C., Zhu, X., Gao, F., Chou, B., Pérez, D., Li, J., Shen, Y., Koperski, K., & Marchisio, G. (2018). Assessment of spatiotemporal fusion algorithms for planet and worldview images. Sensors (Switzerland), 18(4). https://doi.org/10.3390/S18041051
Editor
MDPI
Fecha
2018-03-31
Abstract
[EN] Although Worldview-2 (WV) images (non-pansharpened) have 2-m resolution, the re-visit times for the same areas may be seven days or more. In contrast, Planet images are collected using small satellites that can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It would be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images for applications such as damage assessment, border monitoring, etc. that require quick decisions. In this paper, we evaluate three approaches to fusing Worldview (WV) and Planet images. These approaches are known as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal Data Fusion (FSDAF), and Hybrid Color Mapping (HCM), which have been applied to the fusion of MODIS and Landsat images in recent years. Experimental results using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images.
Materia
Palabras clave
Idioma
eng
Tipo documental
info:eu-repo/semantics/article
Peer review
SI
URI
DOI
Collections
- Untitled [5567]
Files in this item
Tamaño:
1.442Mb
Formato:
Adobe PDF