DPSDA-Net: Dual-Path Convolutional Neural Network with Strip Dilated Attention Module for Road Extraction from High-Resolution Remote Sensing Images

Abstract

Roads extracted from high-resolution remote sensing images are widely used in many fields, such as autonomous driving, road planning, disaster relief, etc. However, road extraction from high-resolution remote sensing images has certain deficiencies in connectivity and completeness due to obstruction by surrounding ground objects, the influence of similar targets, and the slender structure of roads themselves. To address this issue, we propose a novel dual-path convolutional neural network with a strip dilated attention module, named DPSDA-Net, which adopts a U-shaped encoder–decoder structure, combining the powerful advantages of attention mechanism, dilated convolution, and strip convolution. The encoder utilizes ResNet50 as its basic architecture. A strip position attention mechanism is added between each residual block to strengthen the coherent semantic information of a road. A long-distance shortcut connection operation is introduced to preserve the spatial information characteristics of the original image during the downsampling process. At the same time, a pyramid dilated module with a strip convolution and attention mechanism is constructed between the encoder and decoder to enhance the network feature extraction ability and multi-scale extraction of road feature information, expand the model’s receptive field, and pay more attention to the global spatial semantic and connectivity information. To verify the reliability of the proposed model, road extraction was carried out on the Massachusetts dataset and the LRSNY dataset. The experimental results show that, compared with other typical road extraction methods, the proposed model achieved a higher F1 score and IOU. The DPSDA-Net model can comprehensively characterize the structural features of roads, extract roads more accurately, retain road details, and improve the connectivity and integrity of road extraction in remote sensing images.