Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets.

With the popularity of image sensors in various mobile devices, image blurring caused by hand shaking or out of focus becomes ubiquitous, which deteriorates image quality and poses challenges for vision tasks, including object detection, image classification and image segmentation. Designing an efficient blur detection algorithm which can automatically detect and locate blurred regions becomes necessary. In this letter, we design an end-to-end convolution neural network called heterogeneous attention nested U-shaped network (HANUN) for blur detection. We introduce pyramid pooling into encoders to enhance the feature extraction at different scales and reduce the gradual information loss. Inspired by the nested network design, small U-shaped networks are embedded into our decoders to increase the network depth and promote feature fusion with different receptive field scales. In addition, we incorporate a channel attention mechanism in the proposed network to highlight the informative features for detecting the blurry regions. Experimental results show that HANUN outperforms other state-of-the-art algorithms for blur detection tasks on public datasets and real-world images.