Kwan Chiman, Zhu Xiaolin, Gao Feng, Chou Bryan, Perez Daniel, Li Jiang, Shen Yuzhong, Koperski Krzysztof, Marchisio Giovanni
Applied Research LLC, Rockville, MD 20850, USA.
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China.
Sensors (Basel). 2018 Mar 31;18(4):1051. doi: 10.3390/s18041051.
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.
尽管Worldview-2（WV）图像（未进行全色锐化）具有2米的分辨率，但同一区域的重访时间可能为七天或更长时间。相比之下，Planet图像是通过小型卫星收集的，这些卫星几乎可以每天覆盖整个地球。然而，Planet图像的分辨率为3.125米。将这两颗卫星的图像融合，以生成高空间分辨率（2米）和高时间分辨率（1天或2天）的图像，用于诸如灾害评估、边境监测等需要快速决策的应用，将是非常理想的。在本文中，我们评估了三种融合Worldview（WV）和Planet图像的方法。这些方法分别是时空自适应反射率融合模型（STARFM）、灵活时空数据融合（FSDAF）和混合颜色映射（HCM），近年来它们已被应用于MODIS和Landsat图像的融合。使用实际的Planet和Worldview图像进行的实验结果表明，上述三种方法具有可比的性能，并且都可以生成高质量的预测图像。
