BSNet: Dynamic Hybrid Gradient Convolution Based Boundary-Sensitive Network for Remote Sensing Image Segmentation

Abstract

Boundary information is essential for the semantic segmentation of remote sensing images. However, most existing methods were designed to establish strong contextual information while losing detailed information, making it challenging to extract and recover boundaries accurately. In this paper, a boundary-sensitive network (BSNet) is proposed to address this problem via dynamic hybrid gradient convolution (DHGC) and coordinate sensitive attention (CSA). Specifically, in the feature extraction stage, we propose dynamic hybrid gradient convolution (DHGC) to replace vanilla convolution, which adaptively aggregates one vanilla convolution kernel and two gradient convolution kernels (GCKs) into a new operator to enhance boundary information extraction. The GCKs are proposed to explicitly encode boundary information, which are inspired by traditional Sobel operators. In the feature recovery stage, the coordinate sensitive attention (CSA) is introduced. This module is used to reconstruct the sharp and detailed segmentation results by adaptively modeling the boundary information and long-range dependencies in the low-level features as the assistance of high-level features. Note that DHGC and CSA are plug-and-play modules. We evaluate the proposed BSNet on three public data sets: the ISPRS 2-D semantic labeling Vaihingen, Potsdam benchmark and iSAID data set. The experimental results indicate that BSNet is a highly effective architecture that produces sharper predictions around object boundaries and significantly improves the segmentation accuracy. Our method demonstrates superior performance on the Vaihingen, Potsdam benchmark and iSAID data set, in terms of the mean F1, with improvements of 4.6%, 2.3% and 2.4% over strong baselines, respectively. The code and models will be made publicly available.