Deep Unfolding Real-Time Super-Resolution Using Subpixel-Shift Twin Image and Convex Self-Similarity Prior

Multi-image super-resolution (MISR) is a critical technique for satellite remote sensing. In the perspective of information, twin-image super-resolution (TISR) is regarded as the most challenging MISR scenario, having crucial applications like the SPOT-5 supermode imaging. In TISR, an image is super-resolved by its subpixel-shift counterpart (i.e., twin image), where the two images are typically offset by half a pixel both horizontally and vertically. We formulate the less investigated TISR using a convex criterion, which is implemented using a novel deep unfolding network. In the unfolding, an embedded simple shift operator trickily addresses the coupled TISR data-fitting terms, and a transformer trained with a convex self-similarity loss function elegantly implements the proximal mapping induced by the TISR regularizer. The proposed convex self-similarity unfolding supermode super-resolution (COSUP) algorithm is interpretable and achieves state-of-the-art performance with very fast millisecond-level computational time. COSUP is also tested on real-world data, for which the subpixel shifts would not be spatially uniform, with results showing great superiority over the official CNES supermode imaging product in terms of credible metrics (e.g., natural image quality evaluator, NIQE). Source codes: https://github.com/IHCLab/COSUP.

COS2A: Conversion from Sentinel-2 to AVIRIS Hyperspectral Data Using Interpretable Algorithm With Spectral-Spatial Duality

The Sentinel-2 satellite, launched by the European Space Agency (ESA), offers extensive spatial coverage and has become indispensable in a wide range of remote sensing applications. However, it just has 12 spectral bands, making substances/objects identification less effective, not mentioning the varying spatial resolutions (10/20/60 m) across the 12 bands. If such a multi-resolution 12-band image can be computationally converted into a hyperspectral image with uniformly high resolution (i.e., 10 m), it significantly facilitates remote identification tasks. Though there are some spectral super-resolution methods, they did not address the multi-resolution issue on one hand, and, more seriously, they mostly focused on the CAVE-level hyperspectral image reconstruction (involving only 31 visible bands) on the other hand, greatly limiting their applicability in real-world remote sensing scenarios. We ambitiously aim to convert Sentinel-2 data directly into NASA's AVIRIS-level hyperspectral image (encompassing up to 172 visible and near-infrared (NIR) bands, after ignoring those absorption/corruption ones). For the first time, this paper solves this specific super-resolution problem (highly ill-posed), allowing all historical Sentinel-2 data to have their corresponding high-standard AVIRIS counterparts. We achieve so by customizing a novel algorithm that introduces deep unfolding regularization and Q-quadratic-norm regularization into the so-called convex/deep (CODE) small-data learning criterion. Based on the derived spectral-spatial duality, the proposed interpretable COS2A algorithm demonstrates superior spectral super-resolution results across diverse land cover types, as validated through extensive experiments.

Quantum-Driven Multihead Inland Waterbody Detection With Transformer-Encoded CYGNSS Delay-Doppler Map Data

Inland waterbody detection (IWD) is critical for water resources management and agricultural planning. However, the development of high-fidelity IWD mapping technology remains unresolved. We aim to propose a practical solution based on the easily accessible data, i.e., the delay-Doppler map (DDM) provided by NASA's Cyclone Global Navigation Satellite System (CYGNSS), which facilitates effective estimation of physical parameters on the Earth's surface with high temporal resolution and wide spatial coverage. Specifically, as quantum deep network (QUEEN) has revealed its strong proficiency in addressing classification-like tasks, we encode the DDM using a customized transformer, followed by feeding the transformer-encoded DDM (tDDM) into a highly entangled QUEEN to distinguish whether the tDDM corresponds to a hydrological region. In recent literature, QUEEN has achieved outstanding performances in numerous challenging remote sensing tasks (e.g., hyperspectral restoration, change detection, and mixed noise removal, etc.), and its high effectiveness stems from the fundamentally different way it adopts to extract features (the so-called quantum unitary-computing features). The meticulously designed IWD-QUEEN retrieves high-precision river textures, such as those in Amazon River Basin in South America, demonstrating its superiority over traditional classification methods and existing global hydrography maps. IWD-QUEEN, together with its parallel quantum multihead scheme, works in a near-real-time manner (i.e., millisecond-level computing per DDM). To broaden accessibility for users of traditional computers, we also provide the non-quantum counterpart of our method, called IWD-Transformer, thereby increasing the impact of this work.

PRIME: Blind Multispectral Unmixing Using Virtual Quantum Prism and Convex Geometry

Multispectral unmixing (MU) is critical due to the inevitable mixed pixel phenomenon caused by the limited spatial resolution of typical multispectral images in remote sensing. However, MU mathematically corresponds to the underdetermined blind source separation problem, thus highly challenging, preventing researchers from tackling it. Previous MU works all ignore the underdetermined issue, and merely consider scenarios with more bands than sources. This work attempts to resolve the underdetermined issue by further conducting the light-splitting task using a network-inspired virtual prism, and as this task is challenging, we achieve so by incorporating the very advanced quantum feature extraction techniques. We emphasize that the prism is virtual (allowing us to fix the spectral response as a simple deterministic matrix), so the virtual hyperspectral image (HSI) it generates has no need to correspond to some real hyperspectral sensor; in other words, it is good enough as long as the virtual HSI satisfies some fundamental properties of light splitting (e.g., non-negativity and continuity). With the above virtual quantum prism, we know that the virtual HSI is expected to possess some desired simplex structure. This allows us to adopt the convex geometry to unmix the spectra, followed by downsampling the pure spectra back to the multispectral domain, thereby achieving MU. Experimental evidence shows great potential of our MU algorithm, termed as prism-inspired multispectral endmember extraction (PRIME).