Verrelst Jochem, Rivera Caicedo Juan Pablo, Vicent Jorge, Morcillo Pallarés Pablo, Moreno José
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Valéncia, Spain.
CONACyT-UAN, Secretaría de Investigación y Posgrado, Universidad Autónoma de Nayarit, Ciudad de la Cultura Amado Nervo, Tepic CP. 63155, Nayarit, Mexico.
Remote Sens (Basel). 2019 Jan 16;11(2):157. doi: 10.3390/rs11020157.
Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra, to produce synthetic CHRIS and HyMap reflectance spectra for any combination of input biophysical variables. Results indicate that: (1) emulation produces surface reflectance data more accurately than interpolation when validating against a separate part of the field dataset; and (2) emulation produces the spectra multiple times (tens to hundreds) faster than interpolation. This technique opens various data processing opportunities, e.g., emulators not only allow rapidly producing large synthetic spectral datasets, but they can also speed up computationally intensive processing routines such as synthetic scene generation. To demonstrate this, emulators were run to simulate hyperspectral imagery based on input maps of a few biophysical variables coming from CHRIS, HyMap and Sentinel-2 (S2). The emulators produced spaceborne CHRIS-like and airborne HyMap-like surface reflectance imagery in the order of seconds, thereby approximating the spectra of vegetated surfaces sufficiently similar to the reference images. Similarly, it took a few minutes to produce a hyperspectral data cube with a spatial texture of S2 and a spectral resolution of HyMap.
收集具有相关生物物理变量的光谱辐射测量数据是光学遥感植被产品开发和验证的重要组成部分。然而，其质量只能在后续分析中进行评估，而且通常需要收集额外的数据，例如填补空白。为了生成类似经验的植被表面反射率数据，我们建议利用仿真，即通过统计学习重建光谱测量。我们使用一个具有相关高光谱星载CHRIS和机载HyMap反射光谱的经验现场数据集，针对经典插值方法评估了仿真，以生成任何输入生物物理变量组合的合成CHRIS和HyMap反射光谱。结果表明：（1）在针对现场数据集的单独部分进行验证时，仿真生成的表面反射率数据比插值更准确；（2）仿真生成光谱的速度比插值快数倍（数十到数百倍）。这项技术开启了各种数据处理机会，例如，模拟器不仅可以快速生成大型合成光谱数据集，还可以加速诸如合成场景生成等计算密集型处理程序。为了证明这一点，运行模拟器以基于来自CHRIS、HyMap和哨兵-2（S2）的一些生物物理变量的输入地图模拟高光谱图像。模拟器在几秒钟内生成了类似星载CHRIS和机载HyMap的表面反射率图像，从而生成了与参考图像足够相似的植被表面光谱。同样，生成具有S2空间纹理和HyMap光谱分辨率的高光谱数据立方体也只需要几分钟。
