用局部特征聚合提升屋顶光伏板检测准确率
Enhanced Rooftop Solar Panel Detection by Efficiently Aggregating Local Features
- 先提取屋顶局部特征，再用VLAD整合为全局特征
- 三座城市检测准确率均超0.9，超越预设阈值
- 新地区仅需少量标注数据即可快速部署
本文提出一种基于卷积神经网络（CNN）的卫星图像屋顶光伏（PV）面板检测方法。利用预训练的CNN模型提取屋顶的局部卷积特征，再通过局部描述符向量聚合（VLAD）技术融合为屋顶级全局特征，进而训练传统机器学习模型识别含与不含光伏板的屋顶图像。在本研究使用的数据集中，所提方法在三个城市中对每种特征提取网络的屋顶光伏分类得分均超过0.9的预设阈值。此外，我们设计了三阶段流程，实现对新城市或区域的高效模型复用，仅需少量标注数据。该方法在多城市屋顶光伏检测任务中展现出显著有效性。
原文摘要 · Abstract (English)
In this paper, we present an enhanced Convolutional Neural Network (CNN)-based rooftop solar photovoltaic (PV) panel detection approach using satellite images. We propose to use pre-trained CNN-based model to extract the local convolutional features of rooftops. These local features are then combined using the Vectors of Locally Aggregated Descriptors (VLAD) technique to obtain rooftop-level global features, which are then used to train traditional Machine Learning (ML) models to identify rooftop images that do and do not contain PV panels. On the dataset used in this study, the proposed approach achieved rooftop-PV classification scores exceeding the predefined threshold of 0.9 across all three cities for each of the feature extractor networks evaluated. Moreover, we propose a 3-phase approach to enable efficient utilization of the previously trained models on a new city or region with limited labelled data. We illustrate the effectiveness of this 3-phase approach for multi-city rooftop-PV detection task.
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