Adaptive Sampling by Dictionary Learning for Hyperspectral Imaging

作者：Yang Mingrui^*; de Hoog Frank; Fan Yuqi; Hu Wen

来源：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4501-4509.

DOI：10.1109/JSTARS.2016.2553520

• Compressive sensing
• dictionary learning
• hyperspectral imaging
• matrix balancing
• robustness
• singular value decomposition

In this paper, we propose a new sampling strategy for hyperspectral signals that is based on dictionary learning and singular value decomposition (SVD). Specifically, we first learn a sparsifying dictionary fromtraining spectral data using dictionary learning. We then perform an SVD on the dictionary and use the first few left singular vectors as the rows of the measurement matrix to obtain the compressive measurements for reconstruction. The proposed method provides significant improvement over the conventional compressive sensing approaches. The reconstruction performance is further improved by reconditioning the sensing matrix using matrix balancing. We also demonstrate that the combination of dictionary learning and SVDis robust by applying them to different datasets.

• 出版日期2016-9
• 单位CSIRO