Xu Hongwei, Fu Ning, Qiao Liyan, Peng Xiyuan
Depart of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China.
Sensors (Basel). 2015 Apr 21;15(4):9305-23. doi: 10.3390/s150409305.
The large volume of hyperspectral images (HSI) generated creates huge challenges for transmission and storage, making data compression more and more important. Compressive Sensing (CS) is an effective data compression technology that shows that when a signal is sparse in some basis, only a small number of measurements are needed for exact signal recovery. Distributed CS (DCS) takes advantage of both intra- and inter- signal correlations to reduce the number of measurements needed for multichannel-signal recovery. HSI can be observed by the DCS framework to reduce the volume of data significantly. The traditional method for estimating endmembers (spectral information) first recovers the images from the compressive HSI and then estimates endmembers via the recovered images. The recovery step takes considerable time and introduces errors into the estimation step. In this paper, we propose a novel method, by designing a type of coherent measurement matrix, to estimate endmembers directly from the compressively observed HSI data via convex geometry (CG) approaches without recovering the images. Numerical simulations show that the proposed method outperforms the traditional method with better estimation speed and better (or comparable) accuracy in both noisy and noiseless cases.
生成的大量高光谱图像（HSI）给传输和存储带来了巨大挑战，使得数据压缩变得越来越重要。压缩感知（CS）是一种有效的数据压缩技术，它表明当信号在某些基下稀疏时，仅需少量测量就能精确恢复信号。分布式压缩感知（DCS）利用信号内部和信号之间的相关性来减少多通道信号恢复所需的测量次数。通过DCS框架可以观测HSI，从而显著减少数据量。传统的估计端元（光谱信息）的方法是先从压缩后的HSI中恢复图像，然后通过恢复后的图像估计端元。恢复步骤需要相当长的时间，并且会在估计步骤中引入误差。在本文中，我们提出了一种新颖的方法，通过设计一种相干测量矩阵，利用凸几何（CG）方法直接从压缩观测到的HSI数据中估计端元，而无需恢复图像。数值模拟表明，在有噪声和无噪声的情况下，该方法在估计速度和精度（更好或相当）方面均优于传统方法。
