Block Multi-Dimensional Attention for Road Segmentation in Remote Sensing Imagery
Sijun Dong, Zhengchao Chen
Abstract
High-resolution remote sensing image (RSI) segmentation is a relatively mature application in various deep learning projects. In this study, aiming at slender objects in road RSIs, BMDANet combines cross-layer information exchange and block multi-dimensional attention (BMDA) module and optimizes road feature extraction by using multi-dimensional information to construct a global attention module. The experimental results based on the Ottawa road dataset show that our algorithm improved the recognition results of the road in RSI, and excelled the existing RSI road segmentation algorithm and reached the state-of-the-art. In addition, based on comparative experiments, the addition of the BMDA module to different algorithms can effectively improve the accuracy of the algorithm. It has proven the effectiveness and embedding of our BMDA module in RSI road segmentation algorithms.