Multiscale Adaptive Decoder and Diversity Selection Network for Road Extraction in Remote Sensing Image

Road extraction has been a common and challenging task in the field of remote sensing images. Due to factors such as the high resolution of remote sensing images and the subtle visibility of road features, existing methods often miss certain areas during detection and extraction. These methods struggle to capture contextual information effectively and tend to exhibit false positives (FPs) and false negatives (FNs) when handling objects of varying sizes. This article proposes a network based on a multiscale adaptive decoder and diversity selection network (MADSNet) to address the issue of inadequate contextual information capture. By leveraging feature diverse selection, the method minimizes errors in distinguishing between road features and background interference. Specifically, the multiscale feature flexible extraction (MFFE) decoder utilizes the relevance inquiry attention (RIA) module and scope flexible fusion (SFF) module to enhance the ability to capture contextual information with relatively low computational demands. The optimal choice graph attention (OCGA) module aggregates neighboring nodes with similar features in a graph structure, improving focus on the single class of roads. Furthermore, a multilevel feature selection (MFS) module is proposed to activate the features relevant to the current stage while suppressing features from other stages and interfering with noise. Quantitative and qualitative experimental results on three public datasets demonstrate that the proposed MADSNet outperforms currently popular methods in terms of performance. The code will be available at https://github.com/Talent02/MADSNet