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- Title
Pansharpening based on convolutional autoencoder and multi-scale guided filter.
- Authors
AL Smadi, Ahmad; Yang, Shuyuan; Kai, Zhang; Mehmood, Atif; Wang, Min; Alsanabani, Ala
- Abstract
In this paper, we propose a pansharpening method based on a convolutional autoencoder. The convolutional autoencoder is a sort of convolutional neural network (CNN) and objective to scale down the input dimension and typify image features with high exactness. First, the autoencoder network is trained to reduce the difference between the degraded panchromatic image patches and reconstruction output original panchromatic image patches. The intensity component, which is developed by adaptive intensity-hue-saturation (AIHS), is then delivered into the trained convolutional autoencoder network to generate an enhanced intensity component of the multi-spectral image. The pansharpening is accomplished by improving the panchromatic image from the enhanced intensity component using a multi-scale guided filter; then, the semantic detail is injected into the upsampled multi-spectral image. Real and degraded datasets are utilized for the experiments, which exhibit that the proposed technique has the ability to preserve the high spatial details and high spectral characteristics simultaneously. Furthermore, experimental results demonstrated that the proposed study performs state-of-the-art results in terms of subjective and objective assessments on remote sensing data.
- Subjects
MULTISPECTRAL imaging; CONVOLUTIONAL neural networks; REMOTE sensing; IMAGE reconstruction
- Publication
EURASIP Journal on Image & Video Processing, 2021, Vol 2021, Issue 1, p1
- ISSN
1687-5176
- Publication type
Article
- DOI
10.1186/s13640-021-00565-3