Abstract In the context of Earth observation, the trade-off between spatial, spectral, and temporal resolution often limits the versatility of remote sensing images in many important applications. In response, this paper introduces a novel deep learning diffusion model, specifically tailored to improve the spatial resolution of the optical products acquired by the Sentinel-3 (S3) satellite. Our framework employs a diffusion probabilistic model, benefiting from the higher spatial resolution of the Sentinel-2 satellite during training via a new multi-modal loss formulation. This ensures consistency with the original S3 images while enhancing the spatial details. Two distinct conditional low-resolution encoders were experimented with, providing insights into their respective contributions to the diffusion process. The efficacy of the proposed model is demonstrated through extensive ablation studies and comparisons with state-of-the-art methods, using both synthetic and real S3 products. The findings indicate that our model successfully improves spatial resolution while maintaining the integrity of the spectral information, contributing to the field of remote sensing single-image super-resolution.

Motivated by the increasing demand for robust segmentation in unlabeled remote sensing data, we propose DAM-Former, a novel UDA model that fuses high-resolution multimodal imagery with multi-temporal multispectral data. Current UDA approaches in remote sensing rarely exploit the complementary strengths of spatial and temporal features. To address this gap, our framework integrates two interconnected branches: a transformer-based network for high-resolution multimodal data and a lightweight convolutional network with temporal attention for multi-temporal imagery. To improve segmentation accuracy and lower noise, the extracted features are robustly combined through a deep temporal fusion module and a new mixed loss with an ensemble pseudo-label strategy. Extensive experiments and an ablation study on the FLAIR-2 dataset demonstrate that DAM-Former outperforms state-of-the-art methods, marking the first in-depth study of temporal information fusion in UDA segmentation for remote sensing data.

We introduce innovative unsupervised domain adaptation (UDA) techniques that leverage the integration of DAFomer and cross-attention mechanisms, tailored to effectively handle multi-modal datasets. We investigate the methods on the FLAIR-1 dataset with different domains, including RGB, NIR bands, and height information. Experimental findings strongly support the idea that integrating inter-modal information significantly enhances segmentation accuracy, making the model more versatile and effective in handling multi-modal datasets across diverse conditions and domains.

Image registration is an essential task in image processing, where the final objective is to geometrically align two or more images. In remote sensing, this process allows comparing, fusing, or analyzing data, especially when multimodal images are used. In addition, multimodal image registration becomes fairly challenging when the images have a significant difference in scale and resolution, together with local small image deformations. For this purpose, this letter presents a novel optical flow (OF)-based image registration network, named the FloU-Net, which tries to further exploit intersensor synergies by means of deep learning. The proposed method is able to extract spatial information from resolution differences and through a U-Net backbone generate an OF field estimation to accurately register small local deformations of multimodal images in a self-supervised fashion. For instance, the registration between Sentinel-2 (S2) and Sentinel-3 (S3) optical data is not trivial, as there are considerable spectral–spatial differences among their sensors. In this case, the higher spatial resolution of S2 results in S2 data being a convenient reference to spatially improve S3 products, as well as those of the forthcoming Fluorescence Explorer (FLEX) mission, since image registration is the initial requirement to obtain higher data processing level products. To validate our method, we compare the proposed FloU-Net with other state-of-the-art techniques using 21 coupled S2/S3 optical images from different locations of interest across Europe. The comparison is performed through different performance measures. Results show that the proposed FloU-Net can outperform the compared methods. The code and dataset are available inhttps://github.com/ibanezfd/FloU-Net.

The availability of geo-referenced coupled data of dif-ferent platforms is essential to train remote sensing (RS) multi-modal classification and bio-phyiscal parameter esti-mation learning methods. To properly develop a general-izing model different scenes and topographies are required. For this purpose, different multi-modal datasets have been published for the last years. Nevertheless, to our knowl-edge there is not any dataset composed of Sentinel-2 (S2) and Sentinel-3 (S3) geo-referenced images. In this paper we present SEN23, a dataset composed of 100 complete multi-spectral S2 and S3 paired images of different locations along Europe from the 2021 summer. The coupled images were obtained with a time difference of three or less days, containing less than a 1 % of cloud coverage and have a resolution difference of × 15. SEN23E is expected to help with the development of new multi-spectral, multi-resolution and multi-modal models for complex tasks which need con-text and complete images. SEN23E will be available at https://github.com/ibanezdf/SEN23E.

Sentinel missions provide widespread opportunities of exploiting inter-sensor synergies to improve the operational monitoring of terrestrial photosynthetic activity and canopy structural variations using vegetation indices (VI). In this context, continuous and consistent temporal data are logically required to rapidly detect vegetation changes across sensors. Nonetheless, the existing temporal limitations inherent to satellite orbits, cloud occlusions, data degradation, and many other factors may severely constrain the availability of data involving multiple satellites. In response, this letter proposes a novel deep 3-D convolutional regression network (3CRN) for temporally enhancing Sentinel-3 (S3) VI by taking advantage of inter-sensor Sentinel-2 (S2) observations. Unlike existing regression and deep learning-based methods, the proposed approach allows convolutional kernels to slide across the temporal dimension to exploit not only the higher spatial resolution of the S2 instrument but also its own temporal evolution to better estimate time-resolved VI in S3. To validate the proposed approach, we built a database made of multiple day-synchronized S2 and S3 operational products from a study area in Extremadura (Spain). The conducted experimental comparison, including multiple state-of-the-art regression and deep learning models, shows the statistically significant advantages of the presented framework. The codes of this work will be made available athttps://github.com/rufernan/3CRN.

Deep learning has certainly become the dominant trend in hyper-spectral (HS) remote sensing image classification owing to its excellent capabilities to extract highly discriminating spatial-spectral features. In this context, transformer networks have recently shown prominent results in distinguishing even the most subtle spectral differences because of their potential to characterize sequential spectral data. Nonetheless, many complexities affecting HS remote sensing data (e.g. atmospheric effects, thermal noise, quantization noise, etc.) may severely undermine such potential since no mode of relieving noisy feature patterns has still been developed within transformer networks. To address the problem, this paper presents a novel masked auto-encoding spectral-spatial transformer (MAEST), which gathers two different collaborative branches: (i) a reconstruction path, which dynamically uncovers the most robust encoding features based on a masking auto-encoding strategy; and (ii) a classification path, which embeds these features onto a transformer network to classify the data focusing on the features that better reconstruct the input. Unlike other existing models, this novel design pursues to learn refined transformer features considering the aforementioned complexities of the HS remote sensing image domain. The experimental comparison, including several state-of-the-art methods and benchmark datasets, shows the superior results obtained by MAEST. The codes of this paper will be available at https://github.com/ibanezfd/MAEST.

Image registration is an essential task in image processing, where the final objective is to align geometrically two or more images. In Remote Sensing this process allows to compare, fusion or analyse data. For this purpose, different methods and techniques have been proposed. In this paper a selection of image registration methods has been compared performing inter-sensor registration between Sentinel-2 (S2) and Sentinel-3 (S3) operational data. Registration between S2 and S3 data is not trivial, as there are considerable spectral-spatial differences among them. Nevertheless, the resolution difference results in S2 products being a convenient reference to improve S3 products spatially. The experimentation has been done using four sample pairs of S2 and S3 operational data and representatives of the main registration algorithms used in the last years. Performance measures and results are shown and discussed to check the accuracy and quality of the selected methods.