Spectral Reconstruction Network From Multispectral Images to Hyperspectral Images: A Multitemporal Case
Tianshuai Li, Tianzhu Liu, Yukun Wang, Xian Li, Yanfeng Gu
Abstract
Hyperspectral satellite data has been widely applied in many fields due to its numerous bands. Along with the advantages of high spectral resolution, hyperspectral satellite data are still limited by some disadvantages of high acquisition cost, low revisiting capability, and low spatial resolution. Compared with hyperspectral satellites, multispectral satellites have a large number, large width, strong coverage and high spatial resolution. Therefore, multispectral data can be used as the input to the spectral reconstruction to obtain hyperspectral data with high temporal resolution. Better hyperspectral data can be obtained by spectral reconstructing with these continuous multi-temporal data than with single-temporal data. A multi-temporal spectral reconstruction network (MTSRN) is proposed in this paper, which is used to reconstruct hyperspectral images from multi-temporal multispectral images. The proposed MTSRN comprises multiple single-temporal spectral reconstruction networks (STSRN) for extracting temporal features and a multi-temporal fusion network (MTFN). The parallel component alternative (PA) post-processing method enhances the physical plausibility of reconstructed hyperspectral data. To demonstrate performance of the proposed method in aspects of multi-temporal reconstruction, experiments are conducted on four multi-temporal hyperspectral and multispectral satellite datasets. The experimental results prove that the proposed MTSRN obtains better spectral reconstruction results compared with the spectral reconstruction method based on single-temporal information.