ABCNet:Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery

Li, Rui and Zheng, Shunyi and Zhang, Ce and Duan, Chenxi and Wang, Libo and Atkinson, Peter (2021) ABCNet:Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 181. pp. 84-98. ISSN 0924-2716

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
ISPRS Journal of Photogrammetry and Remote Sensing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3300/3305
Subjects:
ID Code:
159534
Deposited By:
Deposited On:
13 Sep 2021 09:00
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Oct 2021 06:11