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

Pan-Sharpening via Deep Locally Linear Embedding Residual Network

Photo from wikipedia

The goal of pan-sharpening tasks is to fuse panchromatic (PAN) images and low spatial-resolution (LR) multi-spectral (MS) images for the purpose of aggregating texture and spectral information. Although traditional embedding-based… Click to show full abstract

The goal of pan-sharpening tasks is to fuse panchromatic (PAN) images and low spatial-resolution (LR) multi-spectral (MS) images for the purpose of aggregating texture and spectral information. Although traditional embedding-based pan-sharpening methods achieve competitive results, they are limited by the shallow network and not suitable for large-scale datasets. In this study, we design a novel multi-scale locally linear embedding residual network (LLERN) that consists of two phases: the spectral preservation phase and the structural preservation phase. As the pretreatment of the structural preservation network, the spectral preservation network aims to up-scale the LR MS image while retaining spectral information. The proposed locally linear embedding residual block (LLERB) in the structural preservation phase can search for similar sparse patches from the PAN image space and embed the corresponding local geometric relationship into the residual space to enhance the MS image. Extensive experiments suggest that the proposed LLERN outperforms state-of-the-art methods from visual and quantitative perspectives and confirm the assumption that LR image patches and residual image patches in a local region share a similar manifold structure which can be used to guide deep-learning modeling with improved interpretability. The source code is available at https://github.com/jiaming-wang/LLERN.

Keywords: network; linear embedding; embedding residual; pan; pan sharpening; locally linear

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2022

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.