Core image processing tasks, such as super-resolution, denoising, deblurring, pansharpening, and atmospheric correction, underpin all optical remote sensing (RS) pipelines. Errors at this stage propagate through downstream applications, distorting land-cover maps, change detection, and climate records. Classical physics-based models capture sensor optics, radiometry, and geometry but struggle with complex noise and scene variability. In contrast, deep learning (DL) methods offer powerful data-driven solutions yet often act as closed boxes, ignoring physical constraints and overfitting to spurious patterns. Hybrid DL (HDL) approaches bridge this gap by integrating physical models with neural architectures, combining interpretability and data adaptivity. This article surveys the emerging landscape of HDL methods in RS image processing, outlining their theoretical foundations, motivations, and design philosophies. We categorize fusion strategies, from model-embedded schemes (e.g., plug-and-play (PnP) and unrolling) to model-guided learning (e.g., deep image prior (DIP) and unsupervised frameworks), and discuss how they enhance trust, robustness, and physical consistency in RS image analysis.

In the last decade, novel hyperspectral cameras have been developed with particularly desirable characteristics of compactness and short acquisition time, retaining their potential to obtain spectral/spatial resolution competitive with respect to traditional cameras. However, a computational effort is required to recover an interpretable data cube. In this work we focus our attention on imaging spectrometers based on interferometry, for which the raw acquisition is an image whose spectral component is expressed as an interferogram. Previous works have focused on the inversion of such acquisition on a pixel-by-pixel basis within a Bayesian framework, leaving behind critical information on the spatial structure of the image data cube. In this work, we address this problem by integrating a spatial regularization for image reconstruction, showing that the combination of spectral and spatial regularizers leads to enhanced performances with respect to the pixelwise case. We compare our results with Plug-and-Play techniques, as its strategy to inject a set of denoisers from the literature can be implemented seamlessly with our physics-based formulation of the optimization problem.

This work focuses on an image fusion protocol that combines conventional RGB cameras with multi-aperture devices utilizing Fabry-Perot interferometry, an unconventional way to acquire hyperspectral data with various advantages compared to dispersive spectrometers. The proposed system aims to enhance hyperspectral imaging quality by preserving high-resolution details from conventional cameras while incorporating the specific spectral range captured by the interferometric device, which mitigates the drawbacks associated to the difficulty of manufacturing Fabry-Perot etalons with low thickness. Envisioned for deployment on unmanned aerial vehicles (UAVs) and microsatellites, this compact setup holds promise for applications in environmental monitoring, agriculture, disaster management, and urban planning. The study presents theoretical foundations, addresses challenges, and offers preliminary results, demonstrating the effectiveness of the proposed approach through Bayesian inference.

Spectral information of the scene can be reconstructed from processing observations acquired by interferometric devices. In the case of devices that have multiple wave interference (e.g., Fabry-Pérot etalons), a simple inversion such as inverse Fourier transform (e.g., for Michelson-like interferometers) of the measured interferograms is not straightforward due to the ill-posedness of the problem. In this paper, we represent the system through an ∞-wave model. The spectral reconstruction is done by a model-based approach as we have a good knowledge of the system. Specifically, we propose to use Loris-Verhoeven algorithm with proximal solvers and induced sparsity on the Fourier domain of the desired spectrum. Our proposal is more robust to noise compared to conventional reconstruction algorithms, as demonstrated by experiments that are carried with interferograms computed from real spectral acquisitions.

Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane. Recent commercial cameras have started implementing RGBW patterns, which include some filters with a wideband spectral response together with the more classical RGB ones. This allows for additional light energy to be captured by the relevant pixels and increases the overall SNR of the acquisition. Demosaicking defines reconstructing a multi-spectral image from the raw image and recovering the full color components for all pixels. However, this operation is often tailored for the most widespread patterns, such as the Bayer pattern. Consequently, less common patterns that are still employed in commercial cameras are often neglected. In this work, we present a generalized framework to represent the image formation model of such cameras. This model is then exploited by our proposed demosaicking algorithm to reconstruct the datacube of interest with a Bayesian approach, using a total variation regularizer as prior. Some preliminary experimental results are also presented, which apply to the reconstruction of acquisitions of various RGBW cameras.

There is an increasing industrial and scientific demand for large scale monitoring of gas concentration, e.g., to abide with constraints on pollutants for environmental safety. This paper aims to show the potential of the very recent ImSPOC technology for gas monitoring. We present the ImSPOC operating principle, some of the developed prototypes and two illustrative examples in the monitoring of methane and carbon dioxide in controlled environments.

This study focuses on a novel snapshot Fourier Transform imaging spectrometer based on an array of Fabry-Perot interferometers. This device fully relies on signal processing in order to provide intelligible outputs and thus requires a precise characterisation. In this paper, we present a strategy for estimating the thickness of the Fabry-Perot cavities, as this information is typically not precise or even available. This is a fundamental step for obtaining calibrated acquisitions with this device and for allowing further data processing and analysis. The proposed technique relies on the device optical model and has proven effective with respect to alternative strategies when applied to real acquisitions.

Sub-pixel mapping (SPM) denotes a category of image processing techniques that further enhance the results provided by spectral unmixing algorithms. While the latter is only capable to determine the fractional abundances of classes with an associated spectral signature within a certain area denoted as mixed pixel, SPM can in addition spatially locate each class separately within the mixed pixel itself, enhancing the spatial resolution of its products. Given the demands by both technological and scientific application, various approaches have been proposed; this work will focus on the ones belonging to the domain of variational frameworks, which allow to solve the intrinsic ill-posedness of inverse problems by imposing a regularization based on gradients of the desired output. As the problem of SPM may be also seen as generating a rule to associate each mixed pixel to a specific patch of mosaicked classes, we propose to create an overcomplete dictionary listing them. We present here some first investigation in this direction, by incorporating such dictionary, generated via K-SVD dictionary learning algorithm, in the formulation of an inverse problem. The algorithm is matched with a Isotropic Total Variation (ITV) regularization to provide joint spatial and spectral consistency to the results. Our tests prove that our proposed method provides better SPM product quality compared to its use in insulation and to other state of the art variational framework-based algorithms.

Hyperspectral imaging is an important source of land cover information by virtue of its spectral richness. However, this type of imagery is typically known by its coarse spatial resolution, that is a limiting factor for end-users. Although spectral unmixing techniques can provide subpixellic information by means of abundance fractions for each class in mixed pixels, the spatial distribution of these classes within each pixel is still unknown. Sub-pixel mapping techniques address the above mentioned problem. Nevertheless, the traditional sub-pixel mapping algorithms based on spatial dependence assumptions cannot solve these problems efficiently. Spatial regularization methods have recently been proposed in a way that they can treat each abundances map separately and do not consider spatial correlation between classes. In order to improve sub-pixel mapping accuracy and, consequently, enhance hyperspectral image classification, we propose a sub-pixel mapping method based on isotropic total variation minimization within and between pixels for different classes simultaneously. Experimental results with synthetic data sets show the attributes of using total variation as a prior model, which leads to improve sub-pixel mapping of different classes together.

In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter. In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources. The burden of data downlink is hence significantly reduced at the expense of a more laborious analysis done at the ground segment to estimate the missing information. The reconstruction algorithm estimates the target sharpened image directly instead of decompressing the original sources beforehand; a viable and practical novel solution is also introduced to show the effectiveness of the approach.

Inpainting in hyperspectral imagery is a challenging research area and several methods have been recently developed to deal with this kind of data. In this paper we address missing data restoration via a convex optimization technique with regularization term based on Collaborative Total Variation (CTV). In particular we evaluate the effectiveness of several instances of CTV in conjunction with different dimensionality reduction algorithms.

Variational methods are widely used in image processing for problems ranging from denoising to data fusion. In this paper we focus on a recent regularization method, called Collaborative Total Variation, applied to the hyperspectral pansharpening, which deals with the fusion of low resolution hyperspectral and high resolution panchromatic images. The effectiveness of this novel approach is evaluated for different Collaborative Norms and the assessment is performed on the Pavia University dataset.

Classical pansharpening algorithms constitute a class of image fusion methods that have been widely investigated in the literature. They have been developed for combining a single- and a multichannel image (panchromatic (PAN) and multispectral (MS), respectively), but can be adapted to the sharpening of hyperspectral (HS) data, both through companion PAN and MS images. We focus in this letter on the HS/MS fusion, showing that the assignation of the MS channel to each HS band is a key step, and investigate several alternatives to make this choice. The assignment algorithms are tested in conjunction with both component substitution and multiresolution analysis pansharpening methods and assessed on images acquired by the Hyperion and ALI sensors. The numerical evaluation shows that the best results can be obtained by optimizing the spectral angle mapper metric confirming that classical methods represent a reliable basis for the development of novel sharpening algorithms.

Thermal Sharpening (TS) is usually referred to techniques widely used in several Earth Observation applications in order to increase the spatial resolution of thermal images. Profiting from the particular design of the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor mounted on board of the Suomi National Polar-orbiting Partnership (NPP) satellite, we propose here a new approach for obtaining synthetic thermal data with increased spatial resolution and spectral diversity. The method exploits classical Pansharpening algorithms, which are very popular in the field of Visible and Near-InfraRed (VNIR) image fusion, for combining the VIIRS thermal bands with partially overlapping spectral responses. We evaluate the effectiveness of several algorithms by performing a Reduced Resolution (RR) assessment on VIIRS real data, showing the importance of an adequate knowledge of the sensor characteristics.

Accurate representations of the Earth surface in both spatial and spectral domains are highly desirable in many applications using remotely sensed data. An effective solution is achieved by combining hyperspectral data, which are characterized by a high spectral diversity, with high spatial resolution images, collected by multispectral or panchromatic sensors. In this work, we compare the outcomes provided by fusing single-platform or multi-platform data. We demonstrate that the optimal choice depends on the target spatial resolution to be achieved. To this aim, real images collected by the Hyperion sensor are combined with data acquired by the ALI sensor or the QuickBird sensor assessing the fused outcomes at reduced resolution.