Ji Chaonan, Bachmann Martin, Esch Thomas, Feilhauer Hannes, Heiden Uta, Heldens Wieke, Hueni Andreas, Lakes Tobia, Metz-Marconcini Annekatrin, Schroedter-Homscheidt Marion, Weyand Susanne, Zeidler Julian
German Aerospace Center, German Remote Sensing Data Center, Wessling, Germany.
Geography Department, Humboldt Universität zu Berlin, Berlin, Germany.
Remote Sens Environ. 2021 Dec 1;266:112692. doi: 10.1016/j.rse.2021.112692.
Over the past decades, solar panels have been widely used to harvest solar energy owing to the decreased cost of silicon-based photovoltaic (PV) modules, and therefore it is essential to remotely map and monitor the presence of solar PV modules. Many studies have explored on PV module detection based on color aerial photography and manual photo interpretation. Imaging spectroscopy data are capable of providing detailed spectral information to identify the spectral features of PV, and thus potentially become a promising resource for automated and operational PV detection. However, PV detection with imaging spectroscopy data must cope with the vast spectral diversity of surface materials, which is commonly divided into spectral intra-class variability and inter-class similarity. We have developed an approach to detect PV modules based on their physical absorption and reflection characteristics using airborne imaging spectroscopy data. A large database was implemented for training and validating the approach, including spectra-goniometric measurements of PV modules and other materials, a HyMap image spectral library containing 31 materials with 5627 spectra, and HySpex imaging spectroscopy data sets covering Oldenburg, Germany. By normalizing the widely used Hydrocarbon Index (HI), we solved the intra-class variability caused by different detection angles, and validated it against the spectra-goniometric measurements. Knowing that PV modules are composed of materials with different transparencies, we used a group of spectral indices and investigated their interdependencies for PV detection with implementing the image spectral library. Finally, six well-trained spectral indices were applied to HySpex data acquired in Oldenburg, Germany, yielding an overall PV map. Four subsets were selected for validation and achieved overall accuracies, producer's accuracies and user's accuracies, respectively. This physics-based approach was validated against a large database collected from multiple platforms (laboratory measurements, airborne imaging spectroscopy data), thus providing a robust, transferable and applicable way to detect PV modules using imaging spectroscopy data. We aim to create greater awareness of the potential importance and applicability of airborne and spaceborne imaging spectroscopy data for PV modules identification.
在过去几十年中，由于硅基光伏（PV）组件成本下降，太阳能板已被广泛用于收集太阳能，因此对光伏组件的存在进行远程测绘和监测至关重要。许多研究探讨了基于彩色航空摄影和人工照片判读的光伏组件检测方法。成像光谱数据能够提供详细的光谱信息以识别光伏的光谱特征，从而有可能成为自动和操作性光伏检测的有前景的资源。然而，利用成像光谱数据进行光伏检测必须应对地表材料巨大的光谱多样性，这通常分为光谱类内变异性和类间相似性。我们开发了一种基于机载成像光谱数据利用光伏组件的物理吸收和反射特性来检测光伏组件的方法。实施了一个大型数据库来训练和验证该方法，包括光伏组件和其他材料的光谱测角测量、一个包含31种材料5627个光谱的HyMap图像光谱库以及覆盖德国奥尔登堡的HySpex成像光谱数据集。通过对广泛使用的烃指数（HI）进行归一化，我们解决了由不同检测角度引起的类内变异性，并根据光谱测角测量对其进行了验证。鉴于光伏组件由具有不同透明度的材料组成，我们使用了一组光谱指数，并通过实施图像光谱库研究了它们在光伏检测中的相互依赖性。最后，将六个训练良好的光谱指数应用于在德国奥尔登堡获取的HySpex数据，生成了一幅总体光伏地图。选择了四个子集进行验证，分别获得了总体精度、生产者精度和用户精度。这种基于物理的方法针对从多个平台（实验室测量、机载成像光谱数据）收集的大型数据库进行了验证，从而提供了一种使用成像光谱数据检测光伏组件的稳健、可转移且适用的方法。我们旨在提高人们对机载和星载成像光谱数据在光伏组件识别方面潜在重要性和适用性的认识。
