# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 4 Discussion  
The study in this paper is important because Planet images are collected using CubeSat's and have more frequent revisit times, as compared to the WV images. However, the resolution of Planet is worse than that of WV images. The methods described in this paper can generate a high spatial resolution and high temporal resolution image series by fusing the two satellite images and will have many important applications, including border monitoring, damage assessment, etc. Decision makers can perform accurate situation assessment based on the fused data. Although there are quite a few fusion studies on MODIS and Landsat images, no one has carried out a fusion study for Planet and WV images to the best of our knowledge. We presented three approaches to fusing the Planet and WV images. The approaches are representative algorithms in the literature. STARFM is well-known in the fusion of MODIS and Landsat. FSDAF incorporates clustering information to assist the prediction process, but requires more computations. HCM is a relatively simple and efficient algorithm for prediction. Based on the experimental results on three scenarios near a US-Mexico border area, the improvement of the three fusion approaches over the original Planet images is significant. In particular, the PSNR gains are close to 3 dB (Tables 5-7) for some of the three scenarios. Other performance metrics such as AD, RMSE, SAM, SSIM, Q2N, ERGAS, and CC all show significant improvement over the baseline (bicubic). In addition to the above metrics, we also generated SSIM index maps (Figures 7, 10 and 13), which also show significant visual improvement of the results from HCM, FSDAF, and STARFM over that of the bicubic method. We can also visually see that the prediction images from all of the algorithms can reveal some fine textures that are missing in the Planet images. Moreover, as can be seen from Figures 6, 9 and 12, the fused images have much better visual quality than the original Planet images. Figure 13: SSIM maps of the Runway scenario using different methods. (**a–d**) are the maps for STARFM, FSDAF, HCM, and bicubic, respectively. It is also worth to mention that, from the results in Section 3, one can observe that none of the three methods can perform well under all scenarios. It is therefore important to have a library of algorithms for image fusion. The best algorithm should be selected for a given application. Since the Planet images are top of atmosphere radiance (TOAR), we opted to work in radiance domain by also using WV-2 images (TOAR). The two types of images indeed have magnitude differences in the radiance domain. To overcome this, we carried out some preprocessing. In pansharpening papers, a common practice has been widely used, which is to apply histogram matching [16; 17] between pan band and the multispectral bands. Here, we adopted a similar strategy in order to work directly in radiance domain. The idea is actually very simple. We used W1 as the reference and applied histogram matching to P1 and P2 so that P1 and P2 are matched to W1. Since W1 and W2 are collected at the same time of the day with the same altitude (the Worldview satellite flies over the same area at roughly the same time of the day), they have roughly the same radiance. We then learn the mapping between the adjusted P1 and P2. Finally, we perform the mapping from W1 to W2 by using the learned mapping earlier. It should be noted that, even if we work directly in the reflectance domain, there is no guarantee that the reflectance values between the Planet and Worldview images will be the same. We may still need to perform some histogram matching after atmospheric compensation. This is because the atmospheric compensation procedures in generating the Worldview reflectance images are proprietary.