  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.
