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Open AccessArticle

Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images

1
Applied Research LLC, Rockville, MD 20850, USA
2
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
3
Hydrology & Remote Sensing Lab, USDA ARS, Beltsville, MD 20704, USA
4
Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
5
Digital Globe, Inc., Herndon, VA 20171, USA
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1051; https://doi.org/10.3390/s18041051
Received: 7 March 2018 / Revised: 27 March 2018 / Accepted: 30 March 2018 / Published: 31 March 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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. View Full-Text
Keywords: image fusion; Planet; Worldview; pansharpening; forward prediction; spatiotemporal image fusion; Planet; Worldview; pansharpening; forward prediction; spatiotemporal
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Kwan, C.; Zhu, X.; Gao, F.; Chou, B.; Perez, D.; Li, J.; Shen, Y.; Koperski, K.; Marchisio, G. Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images. Sensors 2018, 18, 1051.

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