Semi-supervised domain adaptation aims to transfer knowledge from a labeled source domain to a scarcely labeled target domain, despite distribution shifts. The challenge becomes greater when source and target data differ in acquisition modality, as in remote sensing where variations in sensor type (e.g., optical vs. radar), spectral properties (e.g., RGB vs. multispectral), or spatial resolution are common. This challenging scenario, known as Semi-Supervised Heterogeneous Domain Adaptation (SSHDA), requires learning across modalities with limited target labels. In this work, we propose SAHARA (Semi-supervised Adaptation in Heterogeneous domains via conditional Adversarial Representation disentanglement and Adaptive pseudo-labeling), a new method for SSHDA that combines conditional adversarial feature adaptation with dynamic pseudo-labeling to learn domain-invariant features and handle extremely scarce target annotations. Experiments on two heterogeneous remote sensing benchmarks for scene classification, conducted with both convolutional and transformer-based backbones, demonstrate that SAHARA consistently outperforms existing SSHDA and semi-supervised methods. The code is available at https: //TO-BE-DISCLOSED-UPON-ACCEPTANCE.

Hyperspectral pansharpening has received much attention in recent years due to technological and methodological advances that open the door to new application scenarios. However, research on this topic is only now gaining momentum. The most popular methods are still borrowed from the more mature field of multispectral pansharpening and often overlook the unique challenges posed by hyperspectral data fusion, such asi)the very large number of bands,ii)the overwhelming noise in selected spectral ranges,iii)the significant spectral mismatch between panchromatic and hyperspectral components,iv)a typically high resolution ratio. Imprecise data modeling especially affects spectral fidelity. Even state-of-the-art methods perform well in certain spectral ranges and much worse in others, failing to ensure consistent quality across all bands, with the risk of generating unreliable results. Here, we propose a hyperspectral pansharpening method that explicitly addresses this problem and ensures uniform spectral quality. To this end, a single lightweight neural network is used, with weights that adapt on the fly to each band. During fine-tuning, the spatial loss is turned on and off to ensure a fast convergence of the spectral loss to the desired level, according to a hysteresis-like dynamic. Furthermore, the spatial loss itself is appropriately redefined to account for nonlinear dependencies between panchromatic and spectral bands. Overall, the proposed method is fully unsupervised, with no prior training on external data, flexible, and low-complexity. Experiments on a recently published benchmarking toolbox show that it ensures excellent sharpening quality, competitive with the state-of-the-art, consistently across all bands. The software code and the full set of results are shared online on https://github.com/giu-guarino/rho-PNN.

Monitoring particulate matter (PM) is of critical importance due to its significant impact on human health. Ground stations provide highly accurate measurements of various pollutants on a local scale. However, the limited distribution of these stations makes achieving global coverage challenging. To address this limitation, satellite imagery serves as a valuable resource, offering wide-area PM estimates in near real-time through abundant data and frequent revisit intervals. In contrast to other studies, this work introduces deep learning (DL) models to estimate ground-level PM concentration maps over Europe. These models rely exclusively on radiance data from the Sentinel-5P satellite, forgoing auxiliary information, such as meteorological data, which are commonly incorporated in similar studies. The proposed approach has demonstrated both robust estimation accuracy and effective generalization capabilities. Furthermore, the estimated PM concentration maps have been validated against ground-based measurements, showing superior performance with respect to widely used models and datasets that consider meteorological inputs. The dataset and the code are available here:https://github.com/antoniomazza88/PMUnet.

In the last years it has been observed a growing interest toward deep leaning techniques for the pansharpening of multiresolution images. Due to the lack of data with ground truth, most deep learning solutions exploit synthetic reduced-resolution data to carry out supervised training. Such an approach, though granting an easy way to step over the lack of labeled data, has shown its limitations due to the statistical mismatch between real full-resolution data and synthetic reduced-resolution data, which eventually impacts on the generalization capacity of the trained models. This has motivated a recent paradigm shift from supervised to unsupervised learning frameworks for pansharpening. Unsupervised schemes, however, involve the definition of more sophisticated loss functions which comprise, at least, two fundamental terms: one responsible for the spectral quality, meant as consistency between the pansharpened image and the input multispectral component; the other accounting for the spatial quality, read as consistency between the output and the panchromatic input. Despite the very good results shown by many such unsupervised solutions, a minor attention has been devoted to the investigation of the interaction between the above mentioned loss terms and to their proper balance to grant stability while pursuing accuracy. This work aims to explore to what extent unsupervised spatial and spectral consistency losses can be reliably combined without impairing quality.

In this paper, a representative set of state-of-the-art methods for hyperspectral pansharpening, comprising both model- and deep learning-based ones, are reviewed and compared on four datasets from the PRISMA mission. The experimental analysis has been carried out using the most credited pansharpening quality indexes, complemented by a subjective visual inspection of sample results. The obtained outcomes have provided us a preview of the strengths and weaknesses of the latest solutions to the problem at hand, paving the way for future research lines from both the methodological and quality assessment perspectives.

This work proposes a hybrid approach to address the pansharpening of hyperspectral images, which mixes the use of a recently proposed CNN-based solution and a classical solution such as the Gram Schmidt Adaptive method (GSA). The hyperspectral datacube is split in two sets of bands, those falling in the visible range and the remaining ones. The first set is pansharpened using the GSA approach which has proven to grant very high quality results in this range. The remaining bands, whose relationship with the panchromatic band is much weaker, undergo a fusion process based on a recently proposed hyperspectral pansharpening method known as Rolling hyperspectral Pansharpening Neural Network (R-PNN). By doing so, we are able to take the best features from both solutions, getting higher quality results compared to the marginal use of any of the two methods.

This work deals with the estimation of the tropospheric vertical column density of nitrogen dioxide from Sentinel-5P radiance data using convolutional neural networks. The current processing chain to retrieve this information from Sentinel-5P data requires a complex, computationally demanding, physical modeling that involves the use of additional side information such as meteorological variables, which are not always available. Therefore, in this proof-of-concept study, we explored the feasibility of an estimation exclusively using radiance data from Sentinel-5P, leveraging on the powerful representational capacity of deep neural networks. Preliminary results are very promising encouraging further investigation.

Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges. It consists in a pixel-level fusion between a lower-resolution hyperspectral datacube and a higher-resolution single-band image, the panchromatic image, with the goal of providing a hyperspectral datacube at panchromatic resolution. Thanks to their powerful representational capabilities, deep learning models have succeeded to provide unprecedented results on many general purpose image processing tasks. However, when moving to domain specific problems, as in this case, the advantages with respect to traditional model-based approaches are much lesser clear-cut due to several contextual reasons. Scarcity of training data, lack of ground-truth, data shape variability, are some such factors that limit the generalization capacity of the state-of-the-art deep learning networks for hyperspectral pansharpening. To cope with these limitations, in this work we propose a new deep learning method which inherits a simple single-band unsupervised pansharpening model nested in a sequential band-wise adaptive scheme, where each band is pansharpened refining the model tuned on the preceding one. By doing so, a simple model is propagated along the wavelength dimension, adaptively and flexibly, with no need to have a fixed number of spectral bands, and, with no need to dispose of large, expensive and labeled training datasets. The proposed method achieves very good results on our datasets, outperforming both traditional and deep learning reference methods. The implementation of the proposed method can be found on https://github.com/giu-guarino/R-PNN.

The recent paradigm shift from model-based to data-driven approaches has involved a growing number of data-fusion tasks. Specifically for pansharpening, unsupervised deep learning methods have been recently explored with the goal of overcoming the generalization limits shown by early pansharpening convolutional neural networks based on supervised training schemes. Furthermore, some of these exploit the target-adaptive modality to face the scarcity of training data. On the downside, combining usupervised training and target adaptivity causes a non-negligible increase of the computational cost. This work presents a new target adaptive scheme that allows to keep limited the computational cost at inference time while preserving accuracy.

This work proposes a simple yet effective method to adapt unsupervised convolutional neural networks for pansharpening of multispectral images to the problem of hyperspectral image pansharpening, i.e., the fusion of a single high-resolution panchromatic band with a low-resolution hyperspectral data cube. This is achieved by means of a PCA transformation which allows to compact the most of the HS image energy in a few bands, which are then suitably super-resolved using a pansharpening network designed for few spectral bands. Our experiments show very encouraging results which compare favorably against the state-of-the-art methods.

This work proposes a simple yet effective method to adapt unsupervised convolutional neural networks from multispectral to hyperspectral pansharpening. Thus, it focuses on the fusion of a single high-resolution panchromatic band with a low-resolution hyperspectral data cube. This is achieved by means of a decorrelation transform, following the principal component analysis approach, which enables the compression of a significant portion of the hyperspectral image energy into a few bands. Afterwards, a suitably adapted pansharpening network designed for four spectral bands is used to super-resolve only the principal components. Experiments demonstrate high performance in both quantitative and qualitative evaluations, favorably comparing against state-of-the-art methods.

In this paper, we present our results related to the definition of a methodology that combines augmented reality (AR) with semantic techniques for the creation of digital stories associated with museum exhibitions. In contrast to traditional AR approaches, we augment real-world elements by supplementing contents of a museum exhibition with additional inputs that provide new and different meanings. In this way we augment a cultural resource with respect to both its presentation and meaning. The methodology is framed in the cultural re-mediation theory and is grounded on a set of ontologies aimed at modelling a cultural resource and correlating it with external multimedia objects and resources. To provide an easy tool for the creation of museum narratives, the methodology makes use of a set of recognised practices widely adopted by museum curators that have been formalised through inference rules. The defined methodology has been experimented in a scenario related to Flemish paintings to validate the augmentation of cultural objects with two different approaches, the first basing on similarities and the second on dissimilarities.