A critical obstacle to achieve semantic segmentation of remote sensing images by the deep convolutional neural network is the requirement of huge pixel-level labels. Taking building extraction as an example, this study focuses on how to effectively apply weakly supervised semantic segmentation (WSSS) to highresolution remote sensing (HR) images with image-level labels, which is a prominent solution for the huge labeling challenge. The widely-used two-step WSSS framework is adopted, in which the pseudo-masks are first produced from image-level labels and followed by a segmentation network trained by the pseudo-masks. In addition, the fully connected conditional random field (CRF) is utilized to explore spatial context in both training and prediction stages. Detailed analyses are implemented on applying WSSS on HR images in terms of producing pseudo-masks, training segmentation network, and optimizing predictions. We show that the trade-off between precision and recall of pseudo-masks, as well as the boundary accuracy and the background, needs to be carefully considered. The benefits of the segmentation network in the two-step framework are demonstrated in comparison to using classification network only for WSSS, and the effects of CRF-loss are identified to be powerful for improving the segmentation network while not appropriate for dense buildings. An overlapping strategy and CRF post-processing are further demonstrated to be effective for optimizing the segmentation results during inferencing. Through deliberate settings, we can generate results comparable to fully supervised on the ISPRS Potsdam and Vaihingen dataset, which is meaningful for promoting WSSS applications for extracting geographic information from HR images.

Semantic segmentation of high-resolution remote sensing images achieved great progress by utilizing deep convolutional neural networks (DCNNs) in recent years. However, the decrease of resolution in the feature map of DCNNs brings about the loss of spatial information and thus leads to the blurring of object boundary and misclassification of small objects. In addition, the class imbalance and the high diversity of geographic objects in high-resolution images exacerbate the performance. To deal with the above problems, we proposed an end-to-end DCNN network named GAMNet to balance the contradiction between global semantic information and local details. An integration of attention and gate module (GAM) is specially designed to simultaneously realize multi-scale feature extraction and boundary recovery. The integration module can be inserted in an encoder-decoder network with skip connection. Meanwhile, a composite loss function is designed to achieve deep supervision of GAM by adding an auxiliary loss, which can help improve the effectiveness of the integration module. The performance of GAMNet is quantitatively evaluated on the ISPRS 2D Semantic Labelling datasets and achieves state-of-the-art performance in comparison with other representative methods.

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