The hyperspectral image consists of a high number of bands with low bandwidth which gives the advantage in the identification and detection of the features in the level of mineral and chemical composition. But the availability of hyperspectral data is very less and is highly expensive when compared to multispectral data. Simulation of hyperspectral data with the existing hyperspectral and multispectral data can be used as an alternative if data availability is less and is cost-effective. A new method is proposed for hyperspectral image simulation with Chebyshev and Spectral Angle Mapper (SAM) distance functions using python programming and its libraries. The process is selecting similar spectra of each pixel. Using normal processing, the data simulation is very time-consuming. By increasing the cores employed with parallel processing in python programming, the hyperspectral data simulation time is decreased exponentially from 19 to 1 h 21 min. The study clearly explains the logic and open source python code for the simulation of hyperspectral data. Data can be simulated with python code by just giving the paths of test Sentinel-2 and reference Sentinel-2, AVIRIS data. The simulated image gave better normalized cross-correlation values when compared with the original Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data.

高光谱图像由大量低带宽波段组成，这在识别和检测矿物和化学成分水平的特征方面具有优势。但是与多光谱数据相比，高光谱数据的可用性非常低，而且成本非常高。如果数据可用性较低且具有成本效益，则可以使用现有的高光谱和多光谱数据模拟高光谱数据作为替代方案。提出了一种使用 Python 编程及其库使用切比雪夫和光谱角度映射器 (SAM) 距离函数进行高光谱图像模拟的新方法。该过程是选择每个像素的相似光谱。使用正常处理，数据模拟非常耗时。通过增加 Python 编程中并行处理使用的内核，高光谱数据模拟时间从 19 分钟成倍减少到 1 小时 21 分钟。该研究清楚地解释了高光谱数据模拟的逻辑和开源python代码。只需给出测试Sentinel-2和参考Sentinel-2、AVIRIS数据的路径，就可以用python代码模拟数据。与原始机载可见光/红外成像光谱仪-下一代 (AVIRIS-NG) 数据相比，模拟图像提供了更好的归一化互相关值。只需给出测试Sentinel-2和参考Sentinel-2、AVIRIS数据的路径，就可以用python代码模拟数据。与原始机载可见光/红外成像光谱仪-下一代 (AVIRIS-NG) 数据相比，模拟图像提供了更好的归一化互相关值。只需给出测试Sentinel-2和参考Sentinel-2、AVIRIS数据的路径，就可以用python代码模拟数据。与原始机载可见光/红外成像光谱仪-下一代 (AVIRIS-NG) 数据相比，模拟图像提供了更好的归一化互相关值。