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.

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.

In the most existing Hyperspectral Image (HSI) denoising methods, Poisson noise is first transformed into Gaussian noise through Anscombe transform and then remove it. However, transform errors may occur that affect the final denoising results when using Anscombe transform. In this paper, we propose a Poissonian hyperspectral image denoising method without using Anscombe transform (WUAT) to directly remove the noise of Poissonian HSI under the maximum a posteriori (MAP) model by finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). The experimental results show that the proposed method can acquire better performance than most state-of-the-art denoising methods.