The capability to generate realistic hyperspectral imagery plays a prominent role in applications to sensor and mission development as well as in the training of machine learning models. Yet, it is a challenging task due to the high dimensionality and complex spectral–spatial structure of the data. This paper proposes a novel unsupervised deep-learning framework for generating realistic hyperspectral imagery based on blind hyperspectral unmixing and denoising diffusion probabilistic models. First, the approach extracts both endmembers and abundance maps from hyperspectral data through a dictionary of hyperspectral unmixing algorithms. The extracted abundances are then used as inputs for a guided diffusion model, which serves as the generative framework with the goal of producing realistic synthetic abundance maps. Finally, the generation of synthetic hyperspectral images is accomplished by integrating the generated abundance maps with the extracted endmember set and by suitably conditioning the probabilistic formulation of the guided diffusion model as a function of the unmixing algorithms in the aforementioned dictionary. By combining a collection of blind linear unmixing techniques with the generative capabilities of diffusion models, the proposed methodology aims to address key challenges in simulating hyperspectral sensor outputs. The methodology was validated experimentally using real satellite hyperspectral imagery from the PRISMA mission of the Italian Space Agency. The results confirm the effectiveness of the approach in generating realistic synthetic hyperspectral images associated with various land-covers. The code is available at: https://github.com/martinapastorino/HSI_DDPM.

This paper provides an overview of the main activities and results of HYPERHEALTH project (funded by the Italian Space Agency). Specific focus of this paper is hyperspectral PRISMA data exploitation, mostly as regards PRISMA-based atmospheric constituent estimation and allergenic vegetation monitoring.

HYPERHEALTH project is co-funded by Italian Space Agency (ASI) in the framework of the "PRISMA Scienza" program. The program supports R&D projects proposed by experts in hyperspectral remote sensing sector from national public research institutions to industries, also in the framework of international partnerships. The aim is designing, developing and testing innovative methods, techniques and algorithms for exploitation of hyperspectral data, with reliable perspectives as to engineering and pre-operational development, thus contributing to the improvement of socio-economic benefits of the end-user community. This paper outlines HYPERHEALTH main goals and activities.

The rapid growth of hyperspectral satellite missions and the subsequent availability of hyperspectral images have encouraged the remote sensing community to investigate their potential in estimating the concentration of gases, as CH4and CO2, which are related to the greenhouse effect. Though satellite hyperspectral sensors are not specifically designed for this purpose, they are expected to complement more specific satellite missions, such as NASA’s OCO-2 and OCO-3, both in terms of enriched temporal sampling and improved spatial resolution. In this work, we present a new method to estimate the column-averaged dry-air mole fraction of CO2from hyperspectral data on a per-pixel basis. The method, which is here tailored to PRISMA images, leverages the spectral radiance samples collected in the SWIR spectral region around the CO2absorption band at 2000 nm. By assuming a linear model to describe the dependence of the observed radiance on the CO2concentration, the estimation problem is reduced tomatched filteringand can be effectively implemented in compliance with the low computational burden required to perform a pixel by pixel analysis. The performance of the presented method is investigated by means of a rigorous, physically based simulator that accurately reproduces the at-sensor radiance allowing one to check the validity of the assumptions and to assess the algorithm accuracy. The results show that the presented algorithm outperforms a benchmark CIBR based approach, which has been proposed in the literature to get fast per-pixel estimates of CO2concentration.

In satellite hyperspectral sensors, a standard procedure based on homogeneous reference sources is used to regularly update the map of defective pixels (DPs). Unfortunately, this procedure often fails to detect some DPs both because they may arise in the interval between two standard calibration steps and because they may be characterized by subtle or unexpected signal values. The resulting hyperspectral image is affected by residual nonuniformity noise which correlates in the along track direction. This noise source reduces the quality of hyperspectral products, such as classification, unmixing and material detection. In this paper, we present a new procedure to find the location of the residual DPs in the detection matrix and we also propose an effective method to estimate the missing radiance values inferring them from the image pixels by leveraging both spatial and spectral correlation. The procedure, here tailored to PRISMA hyperspectral images, is quite general and can be easily adapted to process images recorded by any satellite pushbroom hyperspectral sensor. The improved image quality yielded by the proposed procedure is first demonstratedqualitatively, by comparing the GRX maps on the original image with those obtained after detection of the DPs and correction of their radiance values. The image quality improvement is thenquantifiedon a set of nine PRISMA images, recorded in an interval of about two years, using twoad-hocdefined indexes. The analysis is carried out separately for the VNIR and SWIR spectrometers of the PRISMA mission.