水体高光谱中的混合效应问题是水体定量遥感中的难点。 已有研究表明, 仅依赖标量光谱信息难以解决复杂的水体混合光谱问题。 广域水体污染物除光谱信息之外, 还具有明显的空间分布特性。 充分利用其空间维信息, 可以作为遥感光谱维信息的有益补充, 有利于水体复杂光谱的解混。 以巢湖为例, HJ-1A HSI高光谱数据为数据源, 辅以水面光谱测量数据, 在空间地统计学和遗传算法理论基础上, 利用地统计学中的变异函数模拟相邻空间两像元的分布差异, 将邻域像元空间变异函数作为遗传算法目标函数的约束条件, 建立基于协同克里格遗传算法的湖泊水体高光谱反演混合光谱空间信息分解模型, 并对悬浮物浓度反演结果进行检验。 结果显示, 与常规混合光谱分解模型相比, 混合光谱空间信息分解模型对悬浮物浓度的预测值与实测值相关系数为0.82, 均方根误差9.25 mg·L-1, 相关系数提高了8.9%, 均方根误差下降了2.78 mg·L-1, 表明该模型对悬浮物浓度具有较强的预测能力。 该方法将水体的空间信息与光谱信息有效结合, 可以避免水色参数光谱信号弱导致反演结果失真, 同时由于高光谱波段多、 信息量大, 带来信息提取计算量大而复杂等问题, 也为复杂水体混合光谱模型的求解和模型反演精度的提高提供了有效途径。

The effect of Mixed-hyperspectral in the water is difficult in quantitative remote sensing of water. Studies have shown that the only scalar spectrum information is difficult to solve the problem of complex mixed spectra of water. Besides the spectral information, spatial distribution of information is one of the obvious characteristics of the broad waters pollution, and can be used as a useful complement to the remote sensing information and facilitate water complex spectral unmixing. Taking Chaohu as an example, the paper applies the HJ-1A HSI hyperspectral data and the supplemental surface spectral measurement data to discuss the mixed spectra of lake water by spatial statistics and genetic algorithm theory. By using the spatial variogram of geostatistics to simulate the distribution difference of two adjacent pixels, the space-informational decomposition model of mixed spectral in lake water is established by co-kriging genetic algorithm, which is a improved algorithm applying the spatial variogram function of neighborhood pixel as the constraint of the objective function of the genetic algorithm. Finally, the model inversion results of suspended matter concentration are verified. Compared with the conventional spectral unmixing model, the results show the correlation coefficient of the predicted and measured value of suspended sediment concentration is 0.82, the root mean square error 9.25 mg·L-1 by mixed spectral space information decomposition model, so the correlation coefficient is increased by 8.9%, the root mean square error reduced by 2.78 mg·L-1, indicating that the model of suspended matter concentration has a strong predictive ability. Therefore, the effective combination of spatial and spectral information of water, can avoid inversion result distortion due to weak spectral signal of water color parameters, and large amount of calculation of information extraction because of the high spectral band numbers, and also provides an effective way to solve spectral mixture model of complex water and improve the accuracy of model inversion.