College of Life Information Science & Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2017 Jun 7;17(6):1322. doi: 10.3390/s17061322.
With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. The spectral adaptive grouping distributed compressive sensing (AGDCS) algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The spectral characteristics of hyperspectral data are analyzed and the joint sparse model is constructed. The spectral bands are adaptively grouped and the hyperspectral data are compressed and reconstructed on the basis of grouping. The experimental results showed that, compared with orthogonal matching pursuit (OMP) and gradient projection for sparse reconstruction (GPSR), AGDCS can significantly improve the visual effect of image reconstruction in the spatial domain. The peak signal-to-noise ratio (PSNR) at a low sampling rate (the sampling rate is lower than 0.2) increases by 13.72 dB than OMP and 1.66 dB than GPSR. In the spectral domain, the average normalized root mean square error, the mean absolute percentage error, and the mean absolute error of AGDCS is 35.38%, 31.83%, and 33.33% lower than GPSR, respectively. Additionally, AGDCS can achieve relatively high reconstructed efficiency.
随着高光谱技术的发展，建立一种有效的光谱数据压缩重建方法，能够提高数据存储、传输和保持光谱信息的能力，这对于植被定量遥感研究和应用至关重要。提出了一种基于光谱自适应分组分布式压缩感知（AGDCS）的算法，实现了植物高光谱数据的分布式压缩感知重建。分析了高光谱数据的光谱特征，构建了联合稀疏模型。根据分组对光谱带进行自适应分组，并对高光谱数据进行压缩和重建。实验结果表明，与正交匹配追踪（OMP）和稀疏重建梯度投影（GPSR）相比，AGDCS 可以显著提高图像重建的空间域视觉效果。在低采样率（采样率低于 0.2）下，峰值信噪比（PSNR）比 OMP 提高了 13.72dB，比 GPSR 提高了 1.66dB。在光谱域中，AGDCS 的平均归一化均方根误差、平均绝对百分比误差和平均绝对误差分别比 GPSR 低 35.38%、31.83%和 33.33%。此外，AGDCS 可以实现较高的重建效率。
