ORBNet: Original Reinforcement Bilateral Network for High-Resolution Remote Sensing Image Semantic Segmentation

Abstract

Semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic research in the field of remote sensing image processing. Many current CNN-based methods complete detailed segmentation by building an encoder–decoder network. However, the representative selection features of ground objects are often ignored and the semantic gap between high-level features and low-level features, resulting in redundant information and erroneous annotation results. In this article, we propose an original reinforcement bilateral network (ORBNet) to improve the performance of HRRSIs semantic segmentation. The ORBNet consists of two branches—the detail branch and the semantic branch, which are responsible for extracting low-level features and high-level features, respectively. The feature alignment and fusion (FAF) modules are used to align features at different levels between two branches and produce shallow features and deep features. Furthermore, we use the detail loss in the detail branch to supervise the generation of low-level features. The class-specific discriminative loss is used to help the semantic branch distinguish features of different ground objects. The spatial-channel attention (SCA) modules are used in the feature fusion stage to select representative features. We conducted extensive experiments on two open-source ISPRS remote sensing datasets, and the experimental results verified the superior performance of our ORBNet.

Keywords

• Deep learning
• feature fusion
• semantic segmentation
• spatial-channel attention