ExplainS2A reformulates spectral super-resolution of multispectral images as spatial super-resolution using spectral-spatial duality and solves it with an explainable deep unfolding and fusion network for fast high-fidelity hyperspectral output.
Vertex component analysis: A fast algorithm to unmix hyperspectral data
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
RADAR assigns adversarial LLM agent roles (Politician, Scientist, Judge) to detect omission-based half-truths in fact verification, but the provided full text is from an unrelated hyperspectral imaging paper.
A geometric modeling-based preprocessing algorithm corrects scale variability in hyperspectral images prior to unmixing, yielding around 50% error reduction in abundance estimation across multiple algorithms on synthetic and real datasets.
citing papers explorer
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ExplainS2A: Explainable Spectral-Spatial Duality Model for Fast Transforming Sentinel-2 Image to AVIRIS-Level Hyperspectral Image
ExplainS2A reformulates spectral super-resolution of multispectral images as spatial super-resolution using spectral-spatial duality and solves it with an explainable deep unfolding and fusion network for fast high-fidelity hyperspectral output.
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Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection
RADAR assigns adversarial LLM agent roles (Politician, Scientist, Judge) to detect omission-based half-truths in fact verification, but the provided full text is from an unrelated hyperspectral imaging paper.
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Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance
A geometric modeling-based preprocessing algorithm corrects scale variability in hyperspectral images prior to unmixing, yielding around 50% error reduction in abundance estimation across multiple algorithms on synthetic and real datasets.