Interpretable Landsat-to-Hyperspectral Dual Super-Resolution Without Large Matrix Inversion

Abstract

Direct acquisition of global hyperspectral images (HSIs) is infeasible given contemporary hardware facilities and limited resources, while global hyperspectral monitoring is critical for remote sensing applications. A more economical approach is to interpretablyconvert the global Landsat-8/9 multispectral images into NASA's airborne visible/infrared imaging spectrometer (AVIRIS)-level HSIs. This conversion involves both spatial super-resolution (SpaSR, 30 to 15 m) and the highly ill-posed spectral super-resolution (SpeSR, seven-band to 172-band), referred to as the dual super-resolution (DualSR) as the duality between SpaSR and SpeSR has been derived very recently. Existing SpeSR methods, mostly for reconstructing CAVE-level HSIs (31 visible bands only), are not applicable to AVIRIS-level task (172 visible and near/shortwave-infrared bands), motivating us to customize an interpretable alternating direction method of multipliers network (ADMM-Net) using Woodbury Lemma (W-Lemma) and spectral continuity prior. Also, unlike conventional generative SpaSR, we employ the panchromatic sharpening strategy to recover physically grounded spatial details. This strategy, however, induces very large matrix inversions (LMIs, with dimensionality proportional to the number of pixels) even after applying the W-Lemma. We resolve the dilemma by trickily designing an LMI-free proximal gradient descent network (PGD-Net). Consequently, a breakthrough in terms of complexity and performance is achieved by the proposed PGD-ADMM interpretable network (PAINT). Beyond state-of-the-art performance, PAINT significantly advances the Landsat classification performance (78.98% accuracy and 76.06% kappa) to a new level (92.16% accuracy and 90.98% kappa).