Recent advances in single-and multi-image superresolution have revealed the limitations of classical image similarity metrics (like peak signal-to-noise ratio), as they often fail to align with human perception when evaluating the visual quality of super-resolved outputs.In this paper, we explore how to exploit keypoint-based metrics to evaluate super-resolution image quality.Specifically, we explore two correlated metrics: (i) a multiscale index proposal measure capturing salience of keypoints, and (ii) a repeatability metric quantifying how consistently the corresponding keypoints are identified in super-resolved and ground-truth images.Experiments on several simulated and real-world datasets show that the repeatability correlates with subjective judgments, and multi-scale index proposal can be helpful for difficult datasets when other metrics are insufficient.

Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations. State-of-the-art approaches underpinned with deep learning allow for obtaining outstanding results, generating images of high perceptual quality. However, it often remains unclear whether the reconstructed details are close to the actual ground-truth information and whether they constitute a more valuable source for image analysis algorithms. In the reported work, we address the latter problem, and we present our efforts toward learning super-resolution algorithms in a task-driven way to make them suitable for generating high-resolution images that can be exploited for automated image analysis. In the reported initial research, we propose a methodological approach for assessing the existing models that perform computer vision tasks in terms of whether they can be used for evaluating super-resolution reconstruction algorithms, as well as training them in a task-driven way. We support our analysis with experimental study and we expect it to establish a solid foundation for selecting appropriate computer vision tasks that will advance the capabilities of real-world super-resolution.

Super-resolution is aimed at enhancing image spatial resolution and it has been intensively explored for many years. The recent advancements, underpinned with deep learning, also include techniques developed specifically for hyper-spectral data. However, most of the emerging methods are validated in application-independent scenarios, which often rely on an unrealistic experimental setup—the reconstruction is performed from simulated low-resolution images (degraded from an original image) with the goal of inverting the degradation process and restoring the original image. This leads to over-optimistic assessment of super-resolution capabilities and limits their practical applications. In this paper, we demonstrate task-based validation for different types of hyperspectral PRISMA image super-resolution, including pan-sharpening, fusion of multispectral and hyperspectral data, as well as single-image super-resolution. The obtained results reported in the paper are encouraging and they help better understand the value of super-resolved PRISMA images.

In this paper, we present our study focused on applying a vision transformer-based pansharpening technique to enhance PRISMA satellite hyperspectral data. The PRISMA mission, launched by the Italian Space Agency, captures hyperspectral images comprising visible and near infra-red, as well as short-wave infra-red channels. By integrating the panchromatic image of high spatial resolution with the hyperspectral data of high spectral resolution, the pansharpening process consists in producing spatially-enhanced hyperspectral imagery. Our research involves modifying and adapting the state-of-the-art HyperTransformer architecture to effectively process real-life PRISMA data. The evaluation of our model’s performance utilizes PRISMA L2D data, encompassing simulated low-resolution data and real-life data. We employ quantitative metrics and visual examination to assess the results. We also highlight the importance of choosing right PRISMA data processing level for the pansharpening process. The proposed pansharpening model successfully enhances PRISMA data for practical applications, contributing to the advancement of Earth observation techniques.

Cloud detection is one of the most important image pre-processing steps that can be performed on-board satellites. It may allow us to reduce the amount of data to analyze or downlink by pruning the cloudy areas, or to make the satellites more autonomous through data-driven image acquisition re-scheduling of the areas obscured by clouds. Thus, building the cloud detection algorithms that can be ultimately deployed in orbit became an important research avenue. In this paper, we investigate the robustness of the fully-convolutional neural networks for cloud detection against the atmospheric conditions that resemble real acquisition settings of the Intuition-1 mission. Our experiments, performed over the original and simulated Landsat-8 images, with the latter reflecting target conditions, shed more light on the performance of deep models and showed how can we verify their robustness in Earth observation tasks for which real images do not exist yet.

Super-resolution reconstruction consists in generating a high-resolution image from a single low-resolution image or multiple images presenting the same area of interest. Existing state-of-the-art approaches to single-image and multi-image super-resolution are based on deep learning that requires large amounts of training data. They are commonly obtained by simulating low-resolution images from an original image treated as a high-resolution reference, but such simulation may not reflect the real-life operating conditions. Therefore, a serious obstacle in deploying super-resolution in real-world cases results from the lack of training data that would encompass real low-resolution images coupled with a real high-resolution reference. In this paper, we propose a new data augmentation technique underpinned with learning the relation between high and low resolution. This helps reduce the requirements concerned with the amount of real-life data necessary to train a super-resolution network, while providing higher-quality data for training, compared with the simulated low-resolution images. Our initial experimental results reported in the paper confirm that the proposed approach is suitable for multi-image super-resolution.

Cloud detection is an important pre-processing step that allows us to significantly reduce the amount of satellite imagery which should undergo further processing. In this paper, we investigate the impact of training set selection on the abilities of fully-convolutional neural networks for this task. Our experiments, performed over a range of Landsat-8 satellite images, show that the performance of deep models can substantially vary for different training samples, especially in the case of challenging scenes, such as those capturing snowy areas.