Affiliation:
1. College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China
Abstract
Many hyperspectral image (HSI) super-resolution (SR) methods have been proposed and have achieved good results; however, they do not sufficiently preserve the spectral information. It is beneficial to sufficiently utilize the spectral correlation. In addition, most works super-resolve hyperspectral images using high computation complexity. To solve the above problems, a novel method based on a channel multilayer perceptron (CMLP) is presented in this article, which aims to obtain a better performance while reducing the computational cost. To sufficiently extract spectral features, a local-global spectral integration block is proposed, which consists of CMLP and some parameter-free operations. The block can extract local and global spectral features with low computational cost. In addition, a spatial feature group extraction block based on the CycleMLP framework is designed; it can extract local spatial features well and reduce the computation complexity and number of parameters. Extensive experiments demonstrate that our method achieves a good performance compared with other methods.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Hunan Provincial Natural Science Foundation Project
Changsha Municipal Natural Science Foundation
Scientific Research Project of Hunan Education Department of China
Subject
General Earth and Planetary Sciences
Cited by
1 articles.
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