Satellite-based remote sensing images are essential for Earth surface analysis, serving diverse purposes in both civilian and military domains. Satellite images are used for analysis and decision making and are considered a reliable source of information. Recently, the field of image generation has developed increasingly sophisticated techniques, such as generative neural models, usually known as Generative Adversarial Neural Networks (GAN), to create synthetic images from scratch that appear almost real. Generative models have traditionally been applied to RGB or grayscale images and have been used for generating fake images of faces, animals, or objects. Currently, there are few studies regarding the application of GAN to multispectral satellite images. This work aims to test GAN models against the generation of multispectral satellite images, and in particular, the work explores the ability of the state-of-the-art StyleGAN3 model to produce high-quality synthetic Sentinel-2 images. The work delves into the configuration, training process, and evaluation of StyleGAN3 using custom Sentinel-2 datasets. StyleGAN3 results are compared with those provided by the proGAN model, the only GAN model tested so far on multispectral satellite data. Evaluation methods include visual inspection, spectral signature analysis, and a modified Fréchet Inception Distance for quantitative assessment. Results show that StyleGAN3 outperforms proGAN model exhibiting visually pleasing images. The quantitative comparison shows that StyleGAN3 provides the best results in terms of FID scores, in particular the improvement compared to proGAN increases as the spatial extent and spectral dimension of the generated images increases.

This article deals with the problem of improving the spatial resolution of hyperspectral (HS) data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) mission. For this purpose, higher spatial resolution data from the Sentinel-2 (S2) mission are exploited. Particularly, 10 S2 bands at 10 and 20 m spatial resolution are used to accomplish the PRISMA super-resolution (SR) task. The article presents a new end-to-end procedure, called PRISMA-SR, that starting from the S2 data and the low-resolution PRISMA image, provides a super-resolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA HS sensor. The first step of the PRISMA-SR procedure consists in fusing S2 data at different spatial resolutions to obtain a synthetic MS image with 10 m spatial resolution and 10 spectral bands. Then, an unsupervised procedure is applied to coregister the fused S2 image and the PRISMA image. Finally, the two images at different spatial resolutions are properly combined in order to obtain the super-resolved HS image. Solutions for each step of the PRISMA-SR processing chain are proposed and discussed. Simulated data are used to show the effectiveness of the PRISMA-SR scheme and to investigate the impact on its performance of each step of the processing chain. Real S2 and PRISMA images are finally considered to provide an example of the application of the PRISMA-SR.

The SAP4PRISMA project research activities aimed at supporting the Italian hyperspectral PRISMA mission by developing preliminary processing chains suitable for PRISMA to obtain high level hyperspectral data products for agriculture, land degradation, natural and human hazards.

This paper deals with the use of voltammetric techniques for the qualitative analysis of water, where we focus on the signal analysis approach and its evaluation in terms of discrimination capability. The prototype described in this paper and the signal analysis chain has been designed in the framework of the development of a field instrument for classification and change detection purposes. The common concept in the various approaches presented in the literature lies in the combination of poorly selective sensors for the characterization of liquids. The fundamental idea of this paper is to investigate how an adequate signal processing approach applied to a mature and affordable sensor technique (voltammetry) can address the issue of extracting aggregate chemical information, which is useful to characterize the liquid under measurement. In the proposed approach, dimensionality reduction is performed in a transformed domain via discrete cosine transform with an appropriate selection of a low-dimensionality subset of the transformed coefficients. The novel methodological approach to the signal processing, two application experiments, and the test set that has been built for the experiments are described here. Finally, the capability of discriminating between different kinds of water is discussed.

The SAP4PRISMA is a four year research project which aims at developing algorithms and products for the future PRISMA mission. The project started on May 2010 and is now entering his full activities as the ”PRISMA like” data set has been defined and the test areas were selected. The paper describes the main project objectives and the activities realized in the first 9 months of the project.