Location: Hydrology and Remote Sensing LaboratoryTitle: Assessment of spatiotemporal fusion algorithms for Planet and Worldview images
|Kwan, C. - Applied Research Associates, Inc|
|Zhu, X - University Of Hong Kong|
|Chou, B. - Applied Research Associates, Inc|
|Perez, D. - Old Dominion University|
|Li, J. - Old Dominion University|
|Shen, Y. - Old Dominion University|
|Koperski, K. - Digital Globe, Inc|
|Marchsio, G. - Digital Globe, Inc|
Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/30/2018
Publication Date: 3/31/2018
Citation: Kwan, C., Zhu, X., Gao, F.N., Chou, B., Perez, D., Li, J., Shen, Y., Koperski, K., Marchsio, G. 2018. Assessment of spatiotemporal fusion algorithms for Planet and Worldview images. Sensors. 18(4):1051. https://doi.org/10.3390/s18041051.
Interpretive Summary: This paper concerns the blending of information obtained from two satellite sources: Worldview and Planet. High spatial resolution images from Worldview satellites provide very detailed spatial information (2 meters) for object identification and surface change detection. However, the re-visit time is more than 7 days. The Planet satellites can provide daily, but coarser, resolution images. This paper investigates three data fusion approaches to blend Worldview and Planet images to generate daily 2-m resolution images. Results show that all approaches can generate high quality images and have comparable performance over different landscapes. High spatial and temporal resolution satellite images produced by these approaches would enable accurate crop monitoring at the sub-field scale.
Technical Abstract: Although Worldview (WV) images (non-pansharpened) have 2-meter resolution, the re-visit times for the same areas may be 7 days or more. In contrast, Planet images using small satellites can cover the whole Earth almost daily. However, the resolution of Planet images is 3.125 m. It will be ideal to fuse these two satellites images to generate high spatial resolution (2 m) and high temporal resolution (1 or 2 days) images. In this paper, we present 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. Experiments using actual Planet and Worldview images demonstrated that the three aforementioned approaches have comparable performance and can all generate high quality prediction images.