In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural formulation for this task. This method does not require the presence of pure pixels (i.e., pixels containing a single material) but instead represents endmembers as convex combinations of a few pixels present in the original hyperspectral image. Our approach leverages an entropic gradient descent strategy, which (i) provides better solutions for hyperspectral unmixing than traditional archetypal analysis algorithms, and (ii) leads to efficient GPU implementations. Since running a single instance of our algorithm is fast, we also propose an ensembling mechanism along with an appropriate model selection procedure that make our method robust to hyper-parameter choices while keeping the computational complexity reasonable. By using six standard real datasets, we show that our approach outperforms state-of-the-art matrix factorization and recent deep learning methods. We also provide an open-source PyTorch implementation: https://github.com/inria-thoth/EDAA.
翻译:在本文中,我们引入了一种新的算法,基于对视光超光谱混混的古典分析,假设成份的线性混合。Archettypal分析是这项任务的自然配方。这个方法不需要纯像素的存在(即含有单一材料的像素),而是代表原始超光谱图像中存在的几像素的共振组合。我们的方法利用了一种昆虫梯级下降战略,即(一)为超光谱非混混集提供比传统古典分析算法更好的解决方案,以及(二)导致高效的GPU执行。由于运行一个我们算法的单一实例的速度很快,我们还提议了一个混合机制,同时采用一个适当的模型选择程序,使我们的方法在保持计算复杂性合理的同时,能够对超光度选择进行稳健。通过使用六个标准真实数据集,我们展示了我们的方法超越了状态的艺术矩阵因子化和最近的深层次学习方法。我们还提供了一种开放源PyToch执行: http://github.com-inriat.