Dual-Stream High Resolution Network for Multi-Source Remote Sensing Image Segmentation

Abstract

Recently, the image segmentation has been a significant research direction in the field of optical remote sensing data processing. However, due to the limitation of the optical imaging mechanism, traditional image segmentation methods are not efficient for processing the optical remote sensing images, especially influencing by the complex weather conditions. In order to ensure the classification performance, synthetic aperture radar (SAR) data are employed as complementary to the data procedure for enhancing the capability of land cover interpretation. Then a dual-stream high-resolution network (HRNet) is proposed to combine two types of heterogeneous data (SAR and optical image), and a multi-modal squeeze-and-excitation (SE) module is exploited to make feature maps fused. Experiments show that the proposed method has excellent performance on the remote sensing data acquired by GF2 and GF3 satellites.