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Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insufficient image spatial resolution. For the high-dimensional nature of HSIs and the correlation between the spectra, the existing Super-Resolution (SR) methods for HSIs have the problems of excessive parameter amount and insufficient information complementarity between the spectra. This paper proposes a Multi-Scale Feature Mapping Network (MSFMNet) based on the cascaded residual learning to adaptively learn the prior information of HSIs. MSFMNet simplifies each part of the network into a few simple yet effective network modules. To learn the spatial-spectral characteristics among different spectral segments, a multi-scale feature generation and fusion Multi-Scale Feature Mapping Block (MSFMB) based on wavelet transform and spatial attention mechanism is designed in MSFMNet to learn the spectral features between different spectral segments. To effectively improve the multiplexing rate of multi-level spectral features, a Multi-Level Feature Fusion Block (MLFFB) is designed to fuse the multi-level spectral features. In the image reconstruction stage, an optimized sub-pixel convolution module is used for the up-sampling of different spectral segments. Through a large number of verifications on the three general hyperspectral datasets, the superiority of this method compared with the existing hyperspectral SR methods is proved. In subjective and objective experiments, its experimental performance is better than its competitors.

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In synthetic aperture imaging, an interferometer measures the Fourier transform of the image rather than the image itself. For a two-element interferometer with base line L, the fringe amplitude and phase is the Fourier component at spatial frequency L/λ. For an optical interferometer with a wide optical bandpass, the measured amplitude and phase is the average amplitude and phase over the bandpass. For pupil plane interferometers, the use of a wide bandpass will limit the field of view, while in an image plane interferometer, the use of a wide bandpass will lower the signal-to-noise ratio of the measurement of amplitude and phase. To preserve both signal-to-noise ratio and field of view, the fringe detector in a long base line interferometer (L/D ≫ 1, where L is the base line, D is the collecting aperture) must measure the fringe amplitude and phase at a number of wave-lengths simultaneously. For the Mark III interferometer, we are using a pupil plane configuration with a low dispersion spectrometer (λ/Δλ = 50–250) and a photon counting imaging detector. A PZT controlled mirror is used to vary the optical path length. A microcoded signal processor is used to demodulate the signal to obtain the quadrature components of the complex fringe visibility at each spectral channel.

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