Edge Guidance Network for Semantic Segmentation of High-Resolution Remote Sensing Images

With the improvement of spatial resolution, the conveyed information of remote sensing images has become increasingly intricate. The semantic content of pixels within the same object exhibits considerable variability, while the semantic content of pixels between different objects exhibits significant overlap. However, most existing approaches focus solely on establishing the internal consistency of objects by aggregating global or multi-scale contextual information without adequately considering the orientation and spatially detailed features of the target. Moreover, these methods often overlook the potential of edge information in achieving accurate edge positioning. These defects will adversely affect the accuracy of segmentation. In this paper, we present an Edge Information Guided Network (EIGNet), which leverages edge information to guide the aggregation of rich contextual information for semantic segmentation to improve the segmentation accuracy of high-resolution remote sensing images. Specifically, an Orientation Convolution Module (OCM) is proposed to construct a spatial detail branch for acquiring precise edge information and spatial detail information. To effectively guide the aggregation of spatial detail features and semantic features, we propose a spatial-semantic feature aggregation module (SSFAM). Moreover, to enhance the extraction of long-range dependencies of irregular objects, we propose the Orientation Atrous Convolution Module (OACM), which facilitates the extraction of multi-phase long-range dependencies of objects. The ISPRS Vaihingen and Potsdam datasets are employed to validate the efficacy of the proposed methodology and draw comparisons with various state-of-the-art techniques. The experimental results demonstrate that the proposed method offers distinct advantages.