Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery

Abstract

Semantic segmentation of remote sensing imagery (RSI) has obtained great success with the development of deep convolutional neural networks (DCNNs). However, most of the existing algorithms focus on designing end-to-end DCNNs, but neglecting to consider the difficulty of segmentation in imbalance categories, especially for minority categories in RSI, which limits the performance of RSI semantic segmentation. In this paper, a novel edge guided context aggregation network (EGCAN) is proposed for the semantic segmentation of RSI. The Unet is employed as backbone. Meanwhile, an edge guided context aggregation branch and minority categories extraction branch are designed for a comprehensive enhancement of semantic modeling. Specifically, the edge guided context aggregation branch is proposed to promote entire semantic comprehension of RSI and further emphasize the representation of edge information, which consists of three modules: edge extraction module (EEM), dual expectation maximization attention module (DEMA), and edge guided module (EGM). EEM is created primarily for accurate edge tracking. According to that, DEMA aggregates global contextual features with different scales and the edge features along spatial and channel dimensions. Subsequently, EGM cascades the aggregated features into the decoder process to capture long-range dependencies and further emphasize the error-prone pixels in the edge region to acquire better semantic labels. Besides this, the exploited minority categories extraction branch is presented to acquire rich multi-scale contextual information through an elaborate hybrid spatial pyramid pooling module (HSPP) to distinguish categories taking a small percentage and background. On the Tianzhi Cup dataset, the proposed algorithm EGCAN achieved an overall accuracy of 84.1% and an average cross-merge ratio of 68.1%, with an accuracy improvement of 0.4% and 1.3% respectively compared to the classical Deeplabv3+ model. Extensive experimental results on the dataset released in ISPRS Vaihingen and Potsdam benchmarks also demonstrate the effectiveness of the proposed EGCAN over other state-of-the-art approaches.