IEEE Trans Image Process. 2013 Dec;22(12):5096-110. doi: 10.1109/TIP.2013.2281405. Epub 2013 Sep 11.
We propose and analyze a new model for hyperspectral images (HSIs) based on the assumption that the whole signal is composed of a linear combination of few sources, each of which has a specific spectral signature, and that the spatial abundance maps of these sources are themselves piecewise smooth and therefore efficiently encoded via typical sparse models. We derive new sampling schemes exploiting this assumption and give theoretical lower bounds on the number of measurements required to reconstruct HSI data and recover their source model parameters. This allows us to segment HSIs into their source abundance maps directly from compressed measurements. We also propose efficient optimization algorithms and perform extensive experimentation on synthetic and real datasets, which reveals that our approach can be used to encode HSI with far less measurements and computational effort than traditional compressive sensing methods.
我们提出并分析了一种基于以下假设的新的高光谱图像 (HSI) 模型：整个信号是由少数几个源的线性组合构成的，每个源都有特定的光谱特征，并且这些源的空间丰度图本身是分段平滑的，因此可以通过典型的稀疏模型有效地编码。我们利用这一假设导出了新的采样方案，并给出了重建 HSI 数据和恢复其源模型参数所需的测量数量的理论下界。这使得我们能够直接从压缩测量中对 HSI 进行分割。我们还提出了有效的优化算法，并在合成和真实数据集上进行了广泛的实验，结果表明，与传统的压缩感知方法相比，我们的方法可以用更少的测量和计算资源对 HSI 进行编码。
