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

Inland waterbody detection (IWD), which aims at identifying and mapping waterbodies such as rivers, lakes, and reservoirs, 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 using only the easily accessible delay-Doppler map (DDM) data 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 data). 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. In terms of quantitative evaluation, IWD-QUEEN leads the IWD-Transformer by approximately 7% and 8% in F1-score and Cohen's kappa, respectively, alluding the promising role of QUEEN in achieving high-performance detection. Source codes: https://github.com/IHCLab/IWD-QUEEN.