通过数据降维提升高光谱图像端元提取效率，计算速度大幅降低且不损失精度。
Hyperspectral Image Data Reduction for Endmember Extraction
- 基于线性混合模型，剔除多组分混合像素以实现数据降维。
- 在保持端元提取精度的前提下，计算时间显著减少。
- 适合处理大规模高光谱图像，尤其适用于计算资源有限的场景。
从高光谱图像中提取端元旨在识别场景中物质的光谱特征。近期研究表明，自字典方法可实现高精度提取，但其高计算成本限制了其在大规模图像中的应用。尽管已有多种方法尝试缓解此问题，仍属重大挑战。本文提出一种数据降维策略：假设高光谱图像遵循线性混合模型并满足纯像素假设，通过移除多重端元混合的像素来减少数据量。理论分析表明，该降维步骤能保留靠近端元的像素。基于此，提出一种结合数据降维与基于线性规划的自字典方法。数值实验显示，该方法在不牺牲端元提取精度的前提下，显著降低原始自字典方法的计算时间。
原文摘要 · Abstract (English)
Endmember extraction from hyperspectral images aims to identify the spectral signatures of materials present in a scene. Recent studies have shown that self-dictionary methods can achieve high extraction accuracy; however, their high computational cost limits their applicability to large-scale hyperspectral images. Although several approaches have been proposed to mitigate this issue, it remains a major challenge. Motivated by this situation, this paper pursues a data reduction approach. Assuming that a hyperspectral image follows the linear mixing model with the pure-pixel assumption, we develop a data reduction technique to remove pixels corresponding to mixtures of multiple endmember signatures. We analyze the theoretical properties of this reduction step and show that it preserves pixels that lie close to the endmembers. Building on this result, we propose a data-reduced self-dictionary method that integrates the data reduction with a self-dictionary method based on a linear programming formulation. Numerical experiments demonstrate that the proposed method can substantially reduce the computational time of the original self-dictionary method without sacrificing endmember extraction accuracy.
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