Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network
This work addresses image fusion for remote sensing or similar applications, but it is incremental as it builds on existing CNN-based fusion techniques with specific optimizations.
The paper tackled the problem of fusing multispectral and hyperspectral images to produce high-resolution hyperspectral images, using a 3-D-CNN with dimensionality reduction to reduce computational time and improve robustness to noise. Results on simulated data showed the method is promising compared to conventional methods, particularly when handling noisy hyperspectral images.
In this paper, we propose a method using a three dimensional convolutional neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral (HS) images to obtain a high resolution hyperspectral image. Dimensionality reduction of the hyperspectral image is performed prior to fusion in order to significantly reduce the computational time and make the method more robust to noise. Experiments are performed on a data set simulated using a real hyperspectral image. The results obtained show that the proposed approach is very promising when compared to conventional methods. This is especially true when the hyperspectral image is corrupted by additive noise.