PLS2-Net: A Physics-Guided Liquid Spectral-Sequence Coarse-to-Fine Reconstruction Network for Remote Sensing Spectral Superresolution

Abstract

Hyperspectral remote sensing imagery provides dense spectral measurements that support material identification and fine-grained classification, but it is expensive to acquire and often limited in spatial resolution. On the contrary, RGB imagery is low-cost and easy to capture with rich spatial details, yet its few channels cannot faithfully represent fine spectral signatures. Spectral superresolution (SSR), which reconstructs hyperspectral images from low-spectral observations, is therefore of practical importance but remains highly illposed: multiple high-dimensional spectra may correspond to the same RGB measurement, and narrowband absorption features or abrupt local spectral variations may not be distinguishable in RGB space, causing systematic bias in regions with similar appearance but different materials. Moreover, many existing SSR models cannot adapt their update behavior to input-dependent spectral dynamics, leading to oversmoothing. To address these issues, we propose PLS2-Net, a physics-guided liquid spectral-sequence coarse-to-fine network for remote sensing SSR. PLS2-Net first produces a stable and interpretable coarse spectral skeleton using a multiscale interpretable coarse spectral generation (MICSG) module. It then performs input-driven bidirectional spectral-sequence modeling with a bidirectional liquid spectral dynamics (BiLSDs) module, improving the representation of long-range cross-band dependencies and directional spectral evolution. Finally, a SRF-Based Physical Consistency Constraint Module (SPC) module together with a null-space detail refiner (NDR) enforces forward consistency and compensates for high-frequency details in unobservable directions, enhancing both physical fidelity and detail restoration. Extensive experiments on Pavia University (PaviaU), Chikusei, and Indian Pines demonstrate that PLS2-Net achieves consistently superior reconstruction accuracy over state-of-the-art methods, particularly in spectral-shape preservation and error suppression.