Hyperspectral pan-sharpening: a variational convex constrained formulation to impose parallel level lines, solved with ADMM

In this paper, we address the issue of hyperspectral pan-sharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral image. The problem is addressed under a variational convex constrained formulation. The objective favors high resolution spectral bands with level lines parallel to those of the panchromatic image. This term is balanced with a total variation term as regularizer. Fit-to-P data and fit-to-HX data constraints are effectively considered as mathematical constraints, which depend on the statistics of the data noise measurements. The developed Alternating Direction Method of Multipliers (ADMM) optimization scheme enables us to solve this problem efficiently despite the non differentiabilities and the huge number of unknowns.Comment: 4 pages, detailed version of proceedings of conference IEEE WHISPERS 201

Scene-adapted plug-and-play algorithm with convergence guarantees

Recent frameworks, such as the so-called plug-and-play, allow us to leverage the developments in image denoising to tackle other, and more involved, problems in image processing. As the name suggests, state-of-the-art denoisers are plugged into an iterative algorithm that alternates between a denoising step and the inversion of the observation operator. While these tools offer flexibility, the convergence of the resulting algorithm may be difficult to analyse. In this paper, we plug a state-of-the-art denoiser, based on a Gaussian mixture model, in the iterations of an alternating direction method of multipliers and prove the algorithm is guaranteed to converge. Moreover, we build upon the concept of scene-adapted priors where we learn a model targeted to a specific scene being imaged, and apply the proposed method to address the hyperspectral sharpening problem

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

Evaluation of Pan-Sharpening Techniques Using Lagrange Optimization

Earth’s observation satellites, such as IKONOS, provide simultaneously multispectral and panchromatic images. A multispectral image comes with a lower spatial and higher spectral resolution in contrast to a panchromatic image which usually has a high spatial and a low spectral resolution. Pan-sharpening represents a fusion of these two complementary images to provide an output image that has both spatial and spectral high resolutions. The objective of this paper is to propose a new method of pan-sharpening based on pixel-level image manipulation and to compare it with several state-of-art pansharpening methods using different evaluation criteria.  The paper presents an image fusion method based on pixel-level optimization using the Lagrange multiplier. Two cases are discussed: (a) the maximization of spectral consistency and (b) the minimization of the variance difference between the original data and the computed data. The paper compares the results of the proposed method with several state-of-the-art pan-sharpening methods. The performance of the pan-sharpening methods is evaluated qualitatively and quantitatively using evaluation criteria, such as the Chi-square test, RMSE, SNR, SD, ERGAS, and RASE. Overall, the proposed method is shown to outperform all the existing methods