SINGLE SATELLITE IMAGE SUPER-RESOLUTION
UP TO X8

Liuyun Duan, Michael Swaine, Nicolas Girard, Sebastien Tripodi, Sacha Lepretre

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Liuyun Duan, Michael Swaine, Nicolas Girard, Sebastien Tripodi, Sacha Lepretre. SINGLE SATELLITE IMAGE SUPER-RESOLUTION UP TO X8. IGARSS2025, Aug 2025, Brisbane (AU), Australia. hal-04997941

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# SINGLE SATELLITE IMAGE SUPER-RESOLUTION UP TO X8

Liuyun Duan, Michael Swaine, Nicolas Girard, Sebastien Tripodi, Sacha Lepretre

LuxCarta

Mouans-Sartoux, France

{lyduan, mswaine, ngirard, stripodi, sacha}@luxcarta.com

**Abstract**—Many applications require high-resolution and recent satellite images (e.g. mapping). Although low-resolution images lack spatial resolution, they have high temporal resolution (e.g. 5-day revisit time for Sentinel-2). When the area of interest is not covered by a recent high-resolution image or it is too expensive to acquire, we propose our super-resolution method to up-scale the recent low-resolution image in order to get a high-resolution image version of the input image. We developed a first model capable of increasing the resolution of any satellite image by a factor of 4, and a Sentinel-2-specific model that can increase the resolution by a factor of 8 (from 10 m to 1.25 m). Our method is GAN-based (faster than diffusion models).

**Index Terms**—Super-resolution, GAN, Sentinel-2, S2

## I. INTRODUCTION

Up-to-date mapping requires the most recent imagery; however, the high-resolution (HR) imagery required for precise mapping have much longer revisit time (months) compared to low-resolution imagery such as Sentinel-2 10 m (5 days). A segmentation model having as input a low-resolution (LR) image but outputting a high-resolution segmentation could be built by appending up-sampling layers and training it on high-resolution labels. However, the task to go directly from low-resolution imagery to high-resolution segmentation would be very difficult and would require an extensive labeled dataset, a powerful model and an optimization procedure capable of making such a model converge on that task.

An advantage of creating a super-resolution model is to split the difficulty of the task into more tractable individual tasks: up-scale the low-resolution image then segment the resulting high-resolution image. Importantly, creating a dataset for super-resolution does not require manual labels and is virtually infinite in size given the amount of high-resolution images available (e.g. Pléiades Neo, WorldView-3, SuperView Neo, NAIP [1]). Another advantage is some applications actually require the high-resolution image itself.

Conventional CNN models provide sharper contrast upscaling than classical interpolation methods; however, super-resolution of ×4 or ×8 involves filling in detailed content given hints in the low-resolution image [2]. This requires some kind of image generation constrained on the input image. Sharper image generation started with GANs [3] using a training strategy involving two competing models: an image generation model and a discriminator model whose goal is to critique the first model and thus improve its results. Diffusion models [4, 5] were a subsequent breakthrough as

they generated more diverse images while not being prone to mode collapse during training. However, the iterative diffusion process inherently is much slower than GANs. Given the need for scalability and high throughput in remote sensing applications, we opted to base our method on ESRGAN [6], a GAN framework for super-resolution.

The closest method to ours is Satlas Super Resolution [7] which train an ESRGAN [6] on their own Sentinel-2 images paired with high-resolution NAIP [1] images which only cover the USA. By contrast our dataset for super-resolution covers many different imagery sources, resolutions and countries from around the globe to yield a more generic super-resolution model. Additionally, their ESRGAN up-scales by a factor of 4 while we built a model for up-scaling Sentinel-2 images by 8. Finally, their model only works correctly on Sentinel-2 10 m and does not generalize to other input resolutions in our tests. Our contributions are:

(i) a generic ×4 super-resolution remote sensing model for optical satellite images (up-scales input images with resolution between 1.2 m and 10 m to 30 cm and 2.5 m respectively),

(ii) a ×8 super-resolution Sentinel-2-specific model for the RGB bands of Sentinel-2 imagery (10 m),

## II. METHOD

Our method is based on SRGAN [8] and relative improvements ESRGAN [6]. SRGAN is a generative adversarial network (GAN) approach for single image super-resolution on ground-level images. It uses a generator model that up-scales

<figure><img src="image_2.png" /><figcaption>Fig. 1: ×8 super-resolution of a Sentinel-2 10 m image to 1.25 m, with Google imagery for reference (zoom for details).</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 2: Comparisons of ×4 up-scalings from different sources and resolutions (zoom for details).</figcaption></figure>

its input by a factor of 4. The training uses a perceptual loss consisting of an adversarial loss and a content loss. The adversarial loss ensures that the generated up-scaled images are within the distribution of high-resolution images in the dataset. The content loss makes sure the generated image matches the ground truth high-resolution image, instead of a simple MSE loss, SRGAN defines the VGG loss computed from inner features of a VGG network [9] trained on ImageNet [10]. This yields better texture in the up-scaled image compared to the MSE loss which has a tendency to produce smoothed results.

## A. Dataset

Firstly, we have an internal repository of high-resolution optical images (from 30 cm to 2 m) from across the globe. This data set contains approximately 3600 images ranging from sizes 2000 × 2000 to 60000 × 60000. By down-sampling those we create a synthetic super-resolution dataset tailored to each model targeted input and output resolution.

Furthermore, because pure down-sampling cannot recreate an exact, native low-resolution image, we built a dataset of 11 Sentinel-2 images (10 m) paired with 50 cm aerial images taken close to the nadir. This dataset covers 446.38 km² in total and helps increase performance on Sentinel-2 images. We focus on urban areas, forests, and crop fields. It was manually curated to create sensible pairs of images, essentially managing transient objects in the scene. Both images should be close temporally and capture as much as possible the same state of the scene by meeting a number of criteria:

1. Crop fields must have the same harvesting state, which can change suddenly from one day to the next

2. Free of clouds to avoid inconsistent clouds

3. Similar illumination (i.e. similar time of day)

4. No airports as planes are big enough to change the pixel values of the 10 m imagery but would not be there on the high-resolution image.

As small (<10 m) transient objects (e.g. cars) do not significantly change the pixel values of the 10 m imagery they do not pose a problem. Contrary to airports, harbors are included in the dataset, as large ships do not move as much as airplanes and largely remain still within the Sentinel-2 revisit time of 5 days. For every pair of images, we perform histogram matching to match the aerial image's colors to the Sentinel-2 image.

To increase the image diversity of our dataset, we employed several data augmentation techniques, which essentially synthesize more training samples from available images. We apply random rotation, flip, color jitter, and image compression with random quality.

## B. Model and training setup

Our training setup is based on ESRGAN [6]. We modified the architecture for the ×8 generator model by appending an up-sampling layer and widening inner layers to increase the model capacity. The ×4 model has 17M parameters and the ×8 model has 31M parameters.

The generic ×4 model was trained on the synthetic dataset from pre-trained weights with a patch size of 256 px (which outputs a patch size of 1024 px) and batch size of 16 per GPU on 2 RTX 3090 (24 GB VRAM) for 5 days.

The ×8 Sentinel-2-specific model was trained from scratch (no pre-trained weights available) with a patch size of 256 px (which outputs a patch size of 2048 px) and batch size of 12
<figure><img src="image_4.png" /><figcaption>Fig. 3: Comparison of Sentinel-2 (S2) super-resolution methods on 2 scenes from Long Beach, USA (zoom for details).</figcaption></figure>

per GPU on 2 RTX 3090 (24 GB VRAM). A first training is done only on the synthetic dataset and then further fine-tuning is done on the Sentinel-2-specific paired dataset. The total training time was around 10 days.

### III. RESULTS AND DISCUSSION

We visually compare super-resolution methods with up-scaling ×4 with different image sources and resolutions in 2. Without specific training on satellite data, ResShift [5] improves the contrast of contours while introducing obvious artifacts. ESRGAN [6] minimizes artifacts, though it results in relatively blurred contours. Satlas [7] can have block artifacts and as it was only trained on Sentinel-2 10 m imagery in the USA it does not generalize to other sources and resolutions. Our model restores realistic fine textures and regular geometry shapes for man-made objects without introducing odd artifacts.

For quantitative evaluation, we created a test dataset to evaluate the performance of different ×4 models. The dataset consists of 39 km² satellite images at 50 cm and 96 km² at 1.2 m, gathered from 17 different areas in the world. Using the classical degradation model [12, 13], we synthesized the corresponding low-resolution images. See Table III for the result of several metrics. Our model is comparable to ESRGAN in terms of cPSNR and CLIPScore while producing sharper object contours and finer details visually.

<table><caption>TABLE I: Quantitative comparison on our test dataset.</caption><thead><tr><th>Model</th><th>PSNR</th><th>SSIM</th><th>CLIPScore [14]</th><th>cPSNR [15]</th></tr></thead><tbody><tr><td>Ours</td><td>20.77</td><td>0.54</td><td><strong>0.99</strong></td><td>23.50</td></tr><tr><td>ESRGAN [6]</td><td><strong>21.52</strong></td><td>0.58</td><td><strong>0.99</strong></td><td><strong>24.50</strong></td></tr><tr><td>ResShift [5]</td><td>17.86</td><td>0.38</td><td>0.97</td><td>19.30</td></tr></tbody></table>

Regarding our Sentinel-2-specific ×8 model, the example in an urban area (Fig. 1) shows that while small individual houses are hard to resolve, super-resolution keeps consistency of neighborhood type and boundary. Swimming pools are generally large enough to be generated in the up-scaled image even if their shape does not always match the reality, with ambiguity between round vs square pools.

Generally we observe that objects get a bit larger in the ×8 up-scaled image compared to a ground truth high-resolution image while their semantic, structure and texture are consistent. Indeed in Sentinel-2 imagery for example, the 10 m pixels leave a lot of ambiguity regarding the exact location of the contour. Additionally, the discriminator does not make the difference between a house slightly larger than it should compared to a ground truth high-resolution image: it remains a believable house and is in distribution.

In Fig. 3 we compare different methods for Sentinel-2 super-resolution. In order to compare with GammaEarth [11] (a commercial product for Sentinel-2 ×10 super-resolution using 12 spectral bands), we take an example from their website and download a recent Sentinel-2 image of the same zone as input to our model and Satlas [7]. Satlas [7] can generate the wrong semantic category, for example, a forest in the river. Compared to GammaEarth [11], we observe a similar quality of generated scenes in terms of fine details and geometry regularity of man-made objects while we only use the RGB bands.

<figure><img src="image_5.png" /><figcaption>Fig. 4: Example ×8 up-scaling of a Sentinel-2 10 m image to 1.25 m of a scene in Africa with some small huts. From left to right: Sentinel-2 (April 11, 2024), our generated ×8 super-resolution image, and Pléiades Neo (March 31, 2024) down-sampled to 1.25 m for reference.</figcaption></figure>

We observe that objects smaller than a pixel from the low-resolution image cannot be recovered using super-resolution, see Fig. 4. Indeed, the small huts are not well reconstructed in our super-resolution. This is an expected behavior as a small object is in minority compared to the background in a low-resolution image pixel. However, our super-resolution still generates an image where we observe some kind of building structures distinct from the surrounding forest.
<figure><img src="image_6.png" /><figcaption>Fig. 5: ×8 up-scaling on crop fields of a Sentinel-2 10 m image to 1.25 m, with Google imagery for reference (zoom for details).</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6: Pléiades 1 m image (left) up-scaled ×4 to 25 cm (right)</figcaption></figure>

Conversely we observe that our model does not hallucinate small objects, which is important for downstream tasks such as object detection. For example, we observe that roads in the up-scaled image are free of cars when up-scaling from 10 m (e.g. Fig. 3).

Examples of our super-resolution on vegetation show that vegetation density is respected (see Fig. 7). On crop fields our model generally generates the right pattern/layout of crops (see Fig. 5).

In Fig. 6 we experimented with applying our ×4 model on a 1 m image to up-scale it to 25 cm, beyond the highest resolution of our dataset. We can see super-resolution recovering rooflines, generalizing well to higher resolutions.

<table><caption>TABLE II: Comparing throughput on a laptop RTX 4060 Ti</caption><thead><tr><th>Model</th><th>Ours ×4</th><th>Ours ×8</th><th>ResShift [5]</th></tr></thead><tbody><tr><td>Throughput</td><td>3.9M px/s</td><td>1.9M px/s</td><td>72K px/s</td></tr></tbody></table>

We compare the throughput of different models in Table II. On Sentinel-2 imagery at 10 m our ×8 model covers 3 km² per second.

To justify the need for a Sentinel-2-specific model, in Fig. 7 we compare a ×8 model trained only on our synthetic dataset with our ×8 model further fine-tuned on our Sentinel-2 paired dataset. With the paired dataset, the reconstructed objects have finer contours and better consistency.

Application: building extraction from up-scaled imagery. We show building extraction results in Fig. 8 on different

<figure><img src="image_8.png" /><figcaption>Fig. 7: Sentinel-2 (left) ×8 up-scaling without (middle) and with (right) fine-tuning on the Sentinel-2 paired dataset.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8: Building extractions (zoom for details).</figcaption></figure>

versions of a 50 cm Pléiades image. We first create a synthetic 2 m image via down-sampling. We then run a building extraction method [16] at 50 cm on three versions of the image: bicubic up-sampling from the synthetic 2 m image, our ×4 super-resolution from the synthetic 2 m image, and the native 50 cm image for reference. We observe more inner walls between buildings being extracted using the super-resolution image compared to bicubic.

## IV. CONCLUSION

We trained several models using a GAN approach for remote sensing super-resolution, one of which specialized on Sentinel-2 imagery at 10 m and up-scaling to 1.25 m. Models can up-scale objects at least a few pixels wide in the low-resolution image with realistic finer details without introducing obvious artifacts, while they cannot recover objects smaller than one pixel.

Future work includes tackling the issue of dilated objects in the up-scaled image and creating a remote sensing specialized content loss with a network trained on remote sensing images instead of ImageNet [10].
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