基于深度卷积网络和标签校正策略的航空影像伪监督太阳能电池板映射
Pseudo Supervised Solar Panel Mapping based on Deep Convolutional Networks with Label Correction Strategy in Aerial Images
摘要：
太阳能电池板测绘已经获得了越来越多的兴趣，在可再生能源领域与遥感图像的援助。以前的重要工作是基于使用经典分类器或卷积神经网络（CNN）的完全监督学习，这通常需要手动注释像素地面实况以提供准确的监督。弱监督方法可以接受图像级注释，这有助于降低像素级标记的成本。然而，不可避免的性能差距存在于弱监督和完全监督的方法之间的映射精度。为了解决这个问题，我们提出了一种具有标签校正策略的伪监督深度卷积网络（PS-CNNLC），用于太阳能电池板映射。它结合了弱监管和强监管的优势，提供准确的太阳能电池板提取。首先，用带有图像级标签的正样本和负样本训练卷积神经网络。然后，它用于从随机选择的未标记图像中自动识别更多阳性样本。通过梯度加权类激活映射进一步处理正样本的特征图以生成初始映射结果，由于它们通常是粗糙和不完整的，因此将其作为初始伪标签。设计了一种渐进式标签修正策略，对初始伪标签进行细化，并迭代训练端到端目标映射网络，从而提高模型的可靠性。进行的综合评价和消融研究证实了申报的PS-CNNLC的优越性。
英文摘要：
Solar panel mapping has gained a rising interest in renewable energy field with the aid of remote sensing imagery. Significant previous work is based on fully supervised learning with classical classifiers or convolutional neural networks (CNNs), which often require manual annotations of pixel-wise ground-truth to provide accurate supervision. Weakly supervised methods can accept image-wise annotations which can help reduce the cost for pixel-level labelling. Inevitable performance gap, however, exists between weakly and fully supervised methods in mapping accuracy. To address this problem, we propose a pseudo supervised deep convolutional network with label correction strategy (PS-CNNLC) for solar panels mapping. It combines the benefits of both weak and strong supervision to provide accurate solar panel extraction. First, a convolutional neural network is trained with positive and negative samples with image-level labels. It is then used to automatically identify more positive samples from randomly selected unlabeled images. The feature maps of the positive samples are further processed by gradient-weighted class activation mapping to generate initial mapping results, which are taken as initial pseudo labels as they are generally coarse and incomplete. A progressive label correction strategy is designed to refine the initial pseudo labels and train an end-to-end target mapping network iteratively, thereby improving the model reliability. Comprehensive evaluations and ablation study conducted validate the superiority of the proposed PS-CNNLC.
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