LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function

Photo from wikipedia

Pansharpening is an important application in remote sensing image processing. It can increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the… Click to show full abstract

Pansharpening is an important application in remote sensing image processing. It can increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the same scene, which brings great favor for subsequent processing such as recognition, detection, etc. In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a panchromatic image and a multispectral image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also propose a Toeplitz sparse term for representing the correlation of adjacent bands. The model is convex and solved by the alternating direction method of multipliers which guarantees the convergence of the proposed method. Extensive experiments on many real datasets collected by different sensors demonstrate the effectiveness of the proposed technique as compared with several state-of-the-art pansharpening approaches.

Keywords: kernel hilbert; hilbert space; image; heaviside function

Journal Title: IEEE Transactions on Image Processing
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.