中文摘要：

为建立一种对光谱数据进行有效压缩和重构的方法,在提高数据的储存、传输效率同时能够保持光谱信息对于植物生理生化参数的解译能力。该研究将压缩感知技术引入对植物光谱的压缩和重构,以含水量、类胡萝卜含量和叶绿素含量等植物关键生理生化参数为反演目标,分别采用不同采样率对植物光谱进行压缩重构的试验,在考察植物光谱的谱间数据相关性基础上,分别在原始光谱、光谱指数和反演模型3个层面讨论了信号压缩重构的效果和影响。试验结果表明,对光谱信号的压缩感知重构在3个层面的误差随信号采样率均呈现规律性变化。在原始光谱层面当采样率达到0.25时,原始光谱重构误差能够稳定在2%以内。在光谱指数层面,不同的光谱指数对采样率的敏感程度不同,在控制重构误差低于10%时,含水量、叶绿素和类胡萝卜素的采样率分别要高于0.25、0.15和0.1。在反演模型层面,通过偏最小二乘回归建模,各生理生化光谱指数模型在采样率达到0.25时,重构的归一化均方根误差降低到16.5%以内。因此,该研究提出的基于压缩传感理论的光谱压缩及重构方法在显著减少植物光谱的数据量的同时,可以保持植物光谱关键信息,能够有效支持植物高光谱数据的处理和分析。

英文摘要：

With the development of hyperspectral technology, it is of great significance to establish a specific compression and reconstruction method that can be used for conducting quantitative remote sensing of vegetation. Such a method is expected not only to improve the efficiency of data storage and transmission, but also to maintain the main spectral characteristics in interpreting some physiological and biochemical parameters. In this study, the compressive sensing technique was introduced to the compression and reconstruction of plant spectrum. Three critical physiological and biochemical parameters of plant, i.e. water content, carotenoid content and chlorophyll content, were chosen to test the retrieving efficacy of the proposed method. To facilitate such an analysis, a spectral dataset consisting of 2 500 spectra and corresponding plant physiological and biochemical parameters with multivariate normal distribution was generated by a classic plant leaf radiation model PROSEPCT. Based on the data, the compression and reconstruction method that was specific for vegetation spectral processing was proposed, described and evaluated. To process the spectral dataset using the compressed sensing method, the spectral dataset was firstly sampled by means of constructing random matrix. Then, the sampled data were reconstructed by a classic orthogonal matching pursuit algorithm, and the normalized root mean square error between the reconstructed data and the original data was analyzed. The performance of the method was thoroughly evaluated on 3 different levels： the spectral level, the feature level and the model level. At the spectral level, an error analysis was performed by directly calculating the original spectra and reconstructed spectra of corresponding samples. At the spectral index level, the spectral index was calculated based on both the original spectra and the reconstructed spectra. Then the error of the spectral index was analyzed. At the model level, the retrieving models for water content, carotenoid conten