Papers › Deep SURE for Unsupervised Remote Sensing Image Fusion
Deep SURE for Unsupervised Remote Sensing Image Fusion
Han V. Nguyen; Magnus O. Ulfarsson; Johannes R. Sveinsson; Mauro Dalla Mura
Image fusion is utilized in remote sensing (RS) due to the limitation of the imaging sensor and the high cost of simultaneously acquiring high spatial and spectral resolution images. Optical RS imaging systems usually provide images of high spatial resolution but low spectral resolution and vice versa. Therefore, fusing those images to obtain a fused image having both high spectral and spatial resolution is desirable in many applications. This article proposes a fusion framework using an unsupervised convolutional neural network (CNN) and Stein’s unbiased risk estimate (SURE). We derive a new loss function for a CNN that incorporates a backprojection mean square error (MSE) with SURE to estimate the projected mse between the fused image and the ground truth. The main motivation is that training a CNN with this SURE loss function is unsupervised and avoids overfitting. Experimental results for two fusion examples, multispectral and hyperspectral (MS–HS) image fusion and multispectral and multispectral (MS–MS) image fusion, show that the proposed method yields high-quality fused images and outperforms the competitive methods. Codes are available at https://github.com/hvn2/Deep-SURE-Fusion .
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections