Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper proposes using a NeRF-like approach for generating high-resolution imagery at some time t based off of high-resolution imagery at time t' and low-resolution imagery at t. This is a practically useful application for different problems in conservation and sustainability as the authors note. They compare the proposed method with SR based methods and image fusion models (e.g. cGAN and Pix2Pix) on two datasets.

The approach consists of:
1.) An image encoder that takes the conactenated HR and LR images as input
2.) A decoder that produces pixel level features
3.) A learned positional embedding 
4.) A pixel-wise decoder that produces the desired high-resolution imagery from the pixel level features and position embedding
5.) A discriminator network

The entire process is trained using a cGAN loss and L1 reconstruction loss.

Evaluation is performed with SSIM, FSIM, and LPIPs.
main_review: This is a well written paper with very clear experiments and contributions with immediate practical application in remote sensing.

Questions for the authors:
- Is LPIPs with VGG features acceptable in remote sensing contexts? What is the reason that VGG features from ImageNet should be able to measure visual perception of high-resolution imagery?
- Did you consider using the NIR bands from Sentinel / NAIP? These spectral values may better constrain the output of the model 
- What is the motivation for EA64 vs. EAD?
- Is it possible to evaluate counting based downstream tasks on fMoW?

## Minor Comments

- Line 126-127, should one of the three F_E be F_A and another be F_D (matching Figure 2)?
limitations_and_societal_impact: The authors address the limitations and potential negative societal impacts. While they acknowledge that their method has potential applications in surveillance, high-resolution on demand satellite imagery is both commercially available and available to government level entities, therefore the additional negative impacts of their method are minimal.
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: This paper presents a method to generate high-resolution satellite imagery from a pair of input low resolution image and a reference high resolution image of the same location but taken at a different time instant. The main problem the author(s) are addressing is the lack of high quality satellite imagery(specially temporally), and that can be resolved using multiple low resolution and a reference HR image. The authors claim that such a high resolution image will be useful for object counting tasks, specially over time when the intermediate high-res images are not available. Their model comprises of a feature mapper (for fusion of input low-res and reference high-res image),  feature encoder (for both the Fourier and spatial feature) and finally the synthesiser (inspired by nerf operating in the temporal domain keeping the spatial domain fixed) they curate a dataset from the publicly available datasets or services like google earth and perform their experiments on them. The authors also conduct a Human study for the verification of their approach along with the usual metrics for image similarity. 
main_review: This paper proposes a interesting application of the already well researched problem if image super resolution. The super resolution is applied on the satellite images, where input to the system is a low resolution image and a reference high resolution image, but taken in a different time instant. The reference HR image provides as a guidance to for high quality realistic texture in the final synthesized image. Similar attempts have been done in general Single image SR ( e.g. SRNTT: Image Super-Resolution by Neural Texture Transfer, CVPR19). 

In the paper the LR image corresponds to 10m/pixel (assuming in each spatial dimension) and the HR image is 1m/pixel. The method is trained using a single LR, reference and the target image. It is claimed that having multiple LR image as input does not add to the final reconstruction as the inputs low resolution images across time prove little additional information. However it seems counter-intuitive as you would expect the system to be able to bridge the gap better between the reference HR time t^{prime} and t. Have the authors tried it or there is lack of granular data to do the verification.

Can the authors elaborate more on the image registration between the input LR and input reference HR.  The reference HR image at 1m/pix resolution might contain objects like vehicles which might not be present in the LR time or not visible in the lower resolution. Does that have an impact on the overall performance of the system.

Can the authors provide some information on the ratio of the rectangles (bounded by the latitude/longitude, Appendix section C) of the are of interest  for the LR and the HR image. That give an indication of how much SR (e.g. 4x, 8x,..) is being done, and put this method in the perspective of the general task of Single image SR.

Can the authors please provide some additional experimental details like input image resolutions, output image resolutions (in spatial coordinates)

The paper is clear and easy to understand. Additional materials in Appendix are useful for understanding the experiments and the dataset. 

Regarding the human evaluation of object counting task, the HR (at t^{prime}) is expected to be different at different time stamp (t). However for the building counting task, the reference HR(t^{prime}) image is significantly different than the target HR(t). This might make the generation task harder.  Can the author please comment on temporal granularity of reference and input images.

Can the authors please comment/discuss on the potential drawbacks of their systems. Some failure examples will be a good to demonstrate the limits of the system, and help further research. Further if they can please comment on how the proposed method will cope with large moving urban artifacts (often captured in satellite images ) like large vehicles, etc.

Can the authors please mention running performance (training times, inference times) for their method.  

In fig. 4, there seems to be a slight colour distortion from the Ground Truth and methods EAD/EA64. However  the baseline methods are closer to ground truth in terms of colour temporature. Can the authors comment if this kind of distortion an atrifact of the system which is guided by reference HR image?

Their model has three part structure, first a feature mapper for combining the LR and ref HR images. Then their encoder combine Fourier the features (as proposed initially by [2]) to allow  their model to learn about temporal variation of the region. The authors take inspiration from the Neural radiance field to generate a temporally novel viewpoint while keeping the spatial dimension fixed. Thus the authors propose a system comprising of well known modules that work and have applied for satellite imagery, which is lacking in novelty. Finally, the resulting image is used for downstream object counting task, where it shows that human subjects perform better with the proposer sure-resolution method. However, this might not be the same for automated object counting. 

limitations_and_societal_impact: 
Can the authors comment on hoe their system will cope with dense urban areas specially areas which have high population where objects in the contention (like buildings) are smaller than dataset. Also how well will this method generalise to different scene with differing urban landscape.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The submission deals with high resolution satellite images, given a low resolution image. The method is trained by learning to predict a high resolution image given a low resolution one, adversiarially, knowing the true high resolution. The point is to use LR imagery to predict HR at points in time where actual HR images are not available. 

Experiments aim at quantifying the quality of generated images, both from a structural and reconstruction quality standpoint, as well as using the generated images in a potential downstream task of object counting, specifically, buildings and swimming pools. 
main_review: The paper deals with an interesting problem and surely deserves some attention. I think the method presented is robust and results encouraging, but I also have some concerns about practical scenarios and settings which maybe should be better presented and motivated in the manuscript.  

In more detail: 
- It is clear that the method aims at good reconstruction, but from the paper is somewhat unclear whether this can be extended from plain RGB to more wavelenghts. For many applications, having near infrared information is crucial, specifically crop monitoring and precision agriculture. In the paper, dataset only aim at reconstructing RGB images, which is a very fair target, but would it perform better if the LR is characterised also by near-infrared information, or if the target HR also contains NIR? I understand only using RGB makes more data available, but in the specific experiments, using sentinel data, other channels would be at hand. 
- I would like to see a better distinction between learninng methods for superresolution. At the end of the day, one can train a SR model on LR-HR pairs, and use it to predict HR from a given LR. I see that standard SR methods do not make use of time, but from the plot in Fig. 5, it does not seem to _that_ relevant. 
- Fig. 5. Is, in facts, statistically significant the difference of using or not using temporal information? I think that the model can struggle not only with ground cover changes (e.g. LL 210-212) but also with seasonal effects, atmospheric effects, sun-angle, etc. These aspects are not well considered in the manuscript and would be very beneficial to discuss these limitations.  The simples example would be the presence of occluding clouds or opaque clouds attenuating signals. 
- In the same figure, I was expecting to see a spike in accuracy after roughly 1y, where the vegetation cycle is roughly at the same point. but I do see a spike at ~510 days, which is weird. What is the cause of that? only a noisy "by chance" spike, or something more? What about crop rotation for this dataset, could it influence the results? This spike is the only place where the time and not time models differ (but also larger error bars -- if the shaded area are std of experiment replica -- it is not explained). 
- Another important point on the same line, is the argument about spatial resolution and the GSD. What is the maximum difference in GSD the model can handle? I am curious since for the swimming pools, LR image resolution (RGB Sentinel 2) often is larger than most small private swimming pools, where a given swimming pool is either contained in a pixel or diluted over a few. How can the model reconstruct such things, if those elements are not visible in the LR images? So I understand one should find best images for the downstream task, and it is obvious that one cannot reconstruct 10cm imagery from 250m GSD MODIS imagery, but some discussion around the issue would be welcome. I think that linking these to the amount of spectral information, could give hints about how to extend the work, since using infrared, for instance, could provide information about land covers not clearly distinguishable in RGB. 
- It is mentioned that land cover changes affect results. This, coupled to the GSD problem of the point above, would suggest that there is a setting in which changes are modes of failures. Can this be a problem also for downstream tasks, and how can one prevent downstream task failures? For instance, not seeing swimming pools in LR data prevents them from being counted, which is a clear limitation.  Also, I can imagine that generating HR images far in the future, would greatly reduce confidence of the objects contained in the HR image. All in all, I wonder (since LR/HR pairs are rarely at uniform time intervals) how this can be detected at inference. 
limitations_and_societal_impact: limitations are not very clearly discussed, while i do not see any potential adverse societal impact.
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final_score: rating: 7: Good paper, accept
review_content: summary: The authors propose a conditional pixel synthesis approach which combines frequent low-resolution aerial imagery with less widely available high-resolution imagery to generate photo-realistic and accurate high-resolution imagery.  The approach is largely based on NeRF with modifications for the remote sensing domain.  Results are compared to other techniques such as image fusion models, Pix2Pix, SuperResolution GAN, and others.
main_review: The authors address an important issue in remote sensing which is the availability of high-resolution imagery.  Frequent, low-resolution imagery is abundant, but high-resolution imagery is much more difficult to obtain.  They use the combination of LR and HR data to produce a photorealistic HR data at the point of interest.  They demonstrate this is both photo-realistic as well as useful for downstream tasks such as counting.

The approach is largely based on NeRF although addresses challenges specific to the remote sensing domain, such as the construction of the 3D scene.  The theoretical derivation is clear, appears correct, and is easy to follow.  However, it would be useful to highlight where the current approach differs from some of the cited related works as NeRF and its derivatives are a relatively new approach.  It is hard to tell which elements are completely new, adapted, or directly used from these other works. 
 The authors reference how their approach differs in relation to SR and Fusion RM approaches, but doesn't draw a direct comparison to what modifications are made relative to the works cited around NeRF and Pixel Synthesis.  Within the derivation, it would be useful to include remarks such as "following the approach of" or "unlike X which did Y", to highlight the originality of the various components in their approach.

Experiments are performed on two datasets with different domains- Texas Housing Dataset which is more "industrial" focused, and Sentinel2 Crop Field Dataset, which has a more "agricultural focus".  This is a very nice addition as it shows the approach is applicable over two domains with likely different statistics (THD likely containing more geometric features than Sentinel2).  Performing a downstream counting task is also powerful in addition to providing a human-perception evaluation.  The only piece I would be interested in seeing is a similar task on the agriculture dataset- there are agricultural metrics and tasks which are often very color-focused as opposed to shape focused.  Even if the images look photo-realistic, slight color variations could be problematic on downstream tasks such as determining crop type, land usage, growth stage, stress/damage, etc.  Those tasks may rely on subtle qualities of the image which the human does not perceive easily.  Performing a similar downstream task on this dataset would be very useful.

Ablation studies are performed to determine the impact on the different components of the framework.  

Regarding both the experiments and ablation studies, while the improvements are so large that it is reasonable to believe they are significantly better there is an increasing desire to see experiments run multiple times so that errorbars around these measurements can be provided and the degree of significance can be determined.

There is always a worry around adversarial examples, especially in a generative setting, especially when there may be surveillance use cases.  It would be interesting to see how small pixel shifts, particularly in the low-resolution imagery, impacts the final generated results.  

The paper is overall clear and well written.  Implementation details are given in the appendix as opposed to the main text, which is suboptimal.  Because all elements enabling reproducibility should be included in the main text, I believe it's important to move as much of that as possible to the body of the text.  


limitations_and_societal_impact: In the Implementation Details I see the hardware used, but do not see any indication as to the time trained (either in epochs/steps, or wall time).  Similarly, there is no mention to inference speed or limitations.  Because this paper is focused on a domain involving huge amounts of data, I believe some discussion around this is warranted.  For example, the authors cite an important usecase of the HR data as exploring poverty metrics.  However, if the cost to run such a model is massive, perhaps there is some drawback to this approach.  

Societal Impacts, particularly in reference to security/surveillance are mentioned, but not discussed at length.  There are some fairly significant implications here both in terms of individual privacy as well as defense/military applications.  Especially regarding the later, generating high-resolution images with military implications could be problematic if the model is incorrect (both in terms of false positives as well as negatives).  Photorealistic images could be generated and shared with the public to bolster support for political agendas- I believe speaking to this even briefly is an important consideration.
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Meta-review: Three of the four reviewers recommended accepting the paper , and one of them increased the score following the rebuttal. I am happy to accept it, I encourage the authors to include the additional material that they discussed in the rebuttal in the final version.
Decision: Accept (Poster)