GraphWS-Net: Weak-Signal Graph Representation Learning for Nonlinear Spectral Unmixing in Complex Remote Sensing Scenes
Keywords:
Graph Neural Networks, Weak-Signal Detection, Nonlinear Spectral Unmixing, Remote Sensing, Representation Learning, Socio-Technical SystemsAbstract
Hyperspectral remote sensing captures rich spectral information across hundreds of contiguous bands, enabling precise material identification and abundance estimation. However, complex scenes often contain materials with weak spectral signatures that are easily masked by dominant endmembers, posing significant challenges for traditional linear and nonlinear unmixing techniques. Existing deep learning approaches, while powerful, frequently overlook the spatial context and inter-pixel relationships critical for capturing weak signals in heterogeneous environments. This paper introduces GraphWS-Net, a novel graph representation learning framework designed for weak-signal nonlinear spectral unmixing. The architecture constructs a graph over the hyperspectral scene where nodes represent pixel spectra and edges encode spatial and spectral affinities. A dedicated weak-signal attention mechanism, integrated with a state-space temporal modeling module, selectively amplifies contributions from low-abundance materials while suppressing noise and dominant spectral interference. The nonlinear unmixing branch operates on graph-convolved features, learning robust abundance maps without requiring explicit mathematical formulations of mixing models. We discuss the system-level design trade-offs, including computational scalability for large-scale deployment, robustness to spectral variability and sensor noise, and fairness considerations across diverse land cover classes. The framework also raises important governance and policy implications for environmental monitoring, agricultural assessment, and urban mapping, particularly regarding data provenance, algorithmic transparency, and equitable resource allocation. Experimental results on both synthetic and real hyperspectral datasets demonstrate that GraphWS-Net significantly outperforms state-of-the-art methods in reconstructing weak-signal abundances while maintaining competitive performance on dominant materials. The proposed approach represents a step toward building trustworthy, deployable unmixing systems for next-generation Earth observation.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
