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<p>Due to low spatial resolution, hyperspectral data often consist of mixtures of contributions from multiple materials. This limitation motivates the task of hyperspectral unmixing (HU), which seeks to identify the spectral signatures (<i>endmembers</i>) of the materials present in an observed scene, along with their relative proportions (<i>fractional abundance</i>) in each pixel. A major challenge in HU arises from class variability among materials, which undermines accurate representation using a single spectral signature, as assumed in the conventional linear mixing model. To address this issue, we propose using group sparsity after representing each material with a set of spectral signatures, known as endmember bundles, where each group corresponds to a specific material. In particular, we develop a bundle-based framework that can enforce either inter-group sparsity or sparsity within and across groups (SWAG) on the abundance coefficients. Furthermore, our framework offers the flexibility to incorporate a variety of sparsity-promoting penalties, among which the transformed <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\ell _1\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>ℓ</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> (TL1) penalty is a novel regularization in the HU literature. Extensive experiments conducted on both synthetic and real hyperspectral data demonstrate the effectiveness and superiority of the proposed approaches.</p>

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A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月17日

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  • Gokul Bhusal,
  • Yifei Lou,
  • Cristina Garcia-Cardona,
  • Ekaterina Merkurjev

摘要

Due to low spatial resolution, hyperspectral data often consist of mixtures of contributions from multiple materials. This limitation motivates the task of hyperspectral unmixing (HU), which seeks to identify the spectral signatures (endmembers) of the materials present in an observed scene, along with their relative proportions (fractional abundance) in each pixel. A major challenge in HU arises from class variability among materials, which undermines accurate representation using a single spectral signature, as assumed in the conventional linear mixing model. To address this issue, we propose using group sparsity after representing each material with a set of spectral signatures, known as endmember bundles, where each group corresponds to a specific material. In particular, we develop a bundle-based framework that can enforce either inter-group sparsity or sparsity within and across groups (SWAG) on the abundance coefficients. Furthermore, our framework offers the flexibility to incorporate a variety of sparsity-promoting penalties, among which the transformed \(\ell _1\) ℓ 1 (TL1) penalty is a novel regularization in the HU literature. Extensive experiments conducted on both synthetic and real hyperspectral data demonstrate the effectiveness and superiority of the proposed approaches.