Li Bo, Yan Lei, Zhang Li-fu
Institute of Remote Sensing & GIS, Peking University, Beijing 100871, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Jul;30(7):1843-7.
Hyperspectral imaging (HSI) has become one of the most promising and emerging techniques in remote sensing. Due to hundreds of co-registered bands used in HSI system, hyperspectral imagery may provide more spectral information than multi-band images. Unfortunately, original hyperspectral images are more expensive and difficult to achieve than multi-band ones. However, an abundance of spectral information has to be acquired by part of special research for the purpose of ground monitoring, which original HSI systems can easily provide. Then a solution, called hyperspectral satellite data simulation, is proposed for studies in satellite data simulation. It is also one of the most important studies to simulate satellite remote sensing data. In the method, the model with low computational complexity can simulate hyperspectral data quickly, which is based on the priori spectral knowledge of the ground objects. But the accuracy of the simulation data depends on spectral parameters of the sensor. In the present paper, the authors experiment with EO-1/ALI bands in VIS/NIR wavelengths. Then the relationship between the spectral parameters, including the number of bands, bandwidth and the peak wavelength, and the simulation accuracy of the vegetation spectrum are analyzed from their variation principles. According to the results, spectral parameters can determine the effective spectral feature of the vegetation, and impact simulation model directly. Optimal parameters are also summarized for spectral reconstruction in the paper. The experiment results are beneficial to enhancing spectral simulation precision. The conclusions can help evaluate the performance of multispectral sensors and perfect spectroscope and filter design.
高光谱成像(HSI)已成为遥感领域最具潜力和新兴的技术之一。由于HSI系统中使用了数百个配准波段,高光谱图像可能比多波段图像提供更多的光谱信息。不幸的是,原始高光谱图像比多波段图像成本更高且难以获取。然而,为了地面监测的目的,必须通过部分专门研究来获取大量光谱信息,而原始HSI系统能够轻松提供这些信息。于是,提出了一种名为高光谱卫星数据模拟的解决方案用于卫星数据模拟研究。它也是模拟卫星遥感数据最重要的研究之一。在该方法中,基于地面物体的先验光谱知识,具有低计算复杂度的模型可以快速模拟高光谱数据。但模拟数据的准确性取决于传感器的光谱参数。在本文中,作者对EO-1/ALI在可见光/近红外波长的波段进行了实验。然后从光谱参数(包括波段数量、带宽和峰值波长)的变化原理分析它们与植被光谱模拟精度之间的关系。根据结果,光谱参数可以确定植被的有效光谱特征,并直接影响模拟模型。本文还总结了用于光谱重建的最佳参数。实验结果有助于提高光谱模拟精度。这些结论有助于评估多光谱传感器的性能,并完善光谱仪和滤波器设计。