基于CNN模型迁移的OLI影像光伏电池板场景识别

获取光伏电池板的空间分布及动态变化信息对于国土调查、资源环境监测和能源结构评估具有重要意义，然而，传统的光伏电池板的识别依赖于人工设计的中低层次特征，无法克服对象光谱不确定性、空间结构类型复杂等难题，算法普遍存在稳健性不强、效率不高等问题。目前，基于场景单元从遥感影像中提取空间信息，多数算法仅建立在少数标准数据集上，未考虑实际应用中遥感图像质量、空间分辨率等因素对图像场景深度特征表达的影响，制约了遥感技术在城市结构、经济社会知识挖掘方面的深入应用。针对以上情况，本文基于卷积神经网络（CNN）采用迁移学习和模型微调的策略，在中等分辨率的Landsat影像上进行光伏电池板场景识别。结果表明，本文方法能够提取电站场景的多层次特征，在形态结构复杂的电站场景中取得了较好的识别效果。

Scene recognition of photovoltaic panels based on model migration and convolution neural network

The acquisition of spatial information of photovoltaic (PV) panels is of great significance to the monitoring of resources and environment and the assessment of energy structure. It has been proved that it is feasible to extract spatial information from remote sensing images based on scene units. The traditional PV power plants recognition depends on the middle and low level characteristics of artificial design. It can not overcome the problems of spectral uncertainty and complex object space structure. The algorithms have problems of low robustness and low efficiency. Although some scholars have used the depth model to classify the image scene, most of the algorithms are based on a few standard remote sensing image scene databases, which do not take into account the influence of the image quality, the scene boundary and the scale on the depth feature of the image scene in the actual application and it restricts the deep application of remote sensing technology in urban structure,economic and social knowledge mining. To this aim, this paper adopts the strategy of transfer learning and model adjustment to identify the scene of PV panels in medium resolution remote sensing images. The results show that the proposed method can extract the multi-level features of the PV panels and achieve good recognition results in the PV panels with complex morphological structure.