Spectral reconstruction (SR) techniques can generate hyperspectral images (HSIs) from multispectral images (MSIs) with the same spatial resolution, thus alleviating the problem of limited availability and low spatial resolution of satellite HSIs. However, in scenarios where both HSIs and MSIs can be acquired simultaneously, spectral mapping relationship (SMR) among real images may not align with the sensor’s spectral response function (SRF), due to factors such as sensor noise and calibration errors. This mismatch can result in discrepancies in reflectivity between the reconstructed HSIs and the real HSIs. To solve the above problems, this article proposes a semi-supervised transfer learning SR (SSTSR) model based on gradient direction constraints. Through semi-supervised learning, SSTSR acquires precise SMRs in overlapping regions and extracts spectral trend information of ground objects from historical models in nonoverlapping regions. Experiments on two datasets demonstrate that the reconstructed HSIs closely resemble real HSIs, leading to impressive classification performance when employing a real HSI classifier.

Hyperspectral (HS) images are widely used to identify and characterize objects in a scene of interest with high acquisition costs and low spatial resolution. It is an inexpensive way to obtain high-spatial-resolution HS images (HSIs) by spectral reconstruction from high-spatial-resolution multispectral (MS) images. In this article, we proposed a progressive spatial–spectral joint network (PSJN) to reconstruct HSIs from MS images. PSJN is composed of a 2-D spatial feature extraction module, a 3-D progressive spatial–spectral feature construction module, and a spectral postprocessing module. PSJN makes full use of the shallow spatial features extracted by the 2-D spatial feature extraction module with the spatial–spectral features extracted by the 3-D progressive spatial–spectral feature construction module. The 3-D progressive spatial–spectral feature construction module is designed to extract spatial–spectral information from local spectra in local space and construct spectral information from a few bands to a lot of bands in a pyramidal structure. Besides, a network updating mechanism is proposed to improve the spectral reconstruction effect of the images with poor original spectral reconstruction effect. The experimental results on three HS–MS datasets and one MS dataset demonstrate the efficacy of the proposed methods. Compared with the most advanced spectral reconstruction methods based on dictionary learning and deep learning, our method achieves the best performance of the latest methods in similarity evaluation and classification performance evaluation.

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.