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.

在过去的几十年中，由于硅基光伏 (PV) 模块的成本降低，太阳能电池板已被广泛用于收集太阳能，因此远程映射和监控太阳能光伏模块的存在至关重要。许多研究探索了基于彩色航拍和手动照片解释的光伏组件检测。成像光谱数据能够提供详细的光谱信息来识别 PV 的光谱特征，因此有可能成为自动化和可操作的 PV 检测的有前途的资源。然而，利用成像光谱数据进行 PV 检测必须应对表面材料的巨大光谱多样性，这通常分为光谱类内可变性和类间相似性。我们开发了一种方法，可以使用机载成像光谱数据根据 PV 模块的物理吸收和反射特性进行检测。实施了一个大型数据库来训练和验证该方法，包括 PV 模块和其他材料的光谱测角测量、包含 31 种材料和 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，并针对光谱测角测量对其进行了验证。知道光伏组件由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对 PV 检测的相互依赖性。最后，将六个训练有素的光谱指数应用于在德国奥尔登堡获得的 HySpex 数据，从而生成整体 PV 地图。选择了四个子集进行验证，分别实现了整体准确度、生产者准确度和用户准确度。这种基于物理学的方法已针对从多个平台（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 PV 模块的稳健、可转移和适用的方法。我们的目标是让人们更加了解机载和星载成像光谱数据对于光伏组件识别的潜在重要性和适用性。