Semantic segmentation of remotely sensed imagery plays a critical role in many real-world applications, such as environmental change monitoring, precision agriculture, environmental protection, and economic assessment. Following rapid developments in sensor technologies, vast numbers of fine-resolution satellite and airborne remote sensing images are now available, for which semantic segmentation is potentially a valuable method. However, because of the rich complexity and heterogeneity of information provided with an ever-increasing spatial resolution, state-of-the-art deep learning algorithms commonly adopt complex network structures for segmentation, which often result in significant computational demand. Particularly, the frequently-used fully convolutional network (FCN) relies heavily on fine-grained spatial detail (fine spatial resolution) and contextual information (large receptive fields), both imposing high computational costs. This impedes the practical utility of FCN for real-world applications, especially those requiring real-time data processing. In this paper, we propose a novel Attentive Bilateral Contextual Network (ABCNet), a lightweight convolutional neural network (CNN) with a spatial path and a contextual path. Extensive experiments, including a comprehensive ablation study, demonstrate that ABCNet has strong discrimination capability with competitive accuracy compared with state-of-the-art benchmark methods while achieving significantly increased computational efficiency. Specifically, the proposed ABCNet achieves a 91.3% overall accuracy (OA) on the Potsdam test dataset and outperforms all lightweight benchmark methods significantly. The code is freely available at https://github.com/lironui/ABCNet.

遥感图像的语义分割在许多实际应用中起着至关重要的作用，例如环境变化监测、精准农业、环境保护和经济评估。随着传感器技术的快速发展，现在可以获得大量高分辨率的卫星和机载遥感图像，语义分割可能是一种有价值的方法。然而，由于空间分辨率不断提高所提供的信息的丰富复杂性和异质性，最先进的深度学习算法通常采用复杂的网络结构进行分割，这通常会导致大量的计算需求。特别，常用的全卷积网络 (FCN) 严重依赖细粒度的空间细节（精细的空间分辨率）和上下文信息（大的感受野），两者都会带来高计算成本。这阻碍了 FCN 在实际应用中的实际效用，尤其是那些需要实时数据处理的应用。在本文中，我们提出了一种新颖的注意力双边上下文网络 (ABCNet)，一种具有空间路径和上下文路径的轻量级卷积神经网络 (CNN)。包括综合消融研究在内的大量实验表明，与最先进的基准方法相比，ABCNet 具有强大的判别能力和具有竞争力的准确性，同时显着提高了计算效率。具体来说，提议的 ABCNet 达到了 91。Potsdam 测试数据集的总体准确率 (OA) 为 3%，明显优于所有轻量级基准测试方法。该代码可在以下位置免费获得https://github.com/lironui/ABCNet。