关键词: 语义分割, 高分辨率影像, 道路提取, 注意力机制, 空间金字塔模型, 卷积神经网络

Abstract:

The extraction of road targets from remote sensing images is of great significance to smart city construction. Due to the complexity and diversity of road and background features in remote sensing data, the accuracy of road extraction using deep learning methods is limited. Therefore, based on the U-Net network architecture design, a deep semantic segmentation model AS-Unet for road extraction from remote sensing images is implemented. The model is divided into two parts：encoder and decoder. The algorithm first adds a channel attention mechanism to the encoder, to filter the extracted rich low-level features, highlight target features, inhibit background noise interference, and improve the accuracy of deep and shallow information fusion. Secondly, considering the single size sensitivity issue of the network to the road targets in the images, a spatial pyramid pooling module is added after the last convolutional layer of the encoding network, to capture road features of different scales. Finally, a spatial attention mechanism is added to the decoder, to further perform learning of location relationship information, and filtering of relevant deep semantic feature, and to improve the ability of feature map restoration. Experiments are conducted on Massachusetts and DeepGlobe road datasets, and the results demonstrate that compared to semantic segmentation networks such as SegNet and FCN, the AS-Unet network is preferable in terms of the evaluation indexes such as recall, precision, and [F1] value. The designed AS-Unet network has satisfactory performance and higher segmentation accuracy, and boasts certain theoretical and practical application value.

Key words: semantic segmentation, high-resolution image, road extraction, attention mechanism, spatial pyramid model, convolutional neural network