ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images

TL;DR

ABCNet is a novel CNN architecture designed for efficient semantic segmentation of high-resolution remote sensing images, balancing low computational cost with competitive accuracy for real-world applications.

Contribution

The paper introduces ABCNet, a dual-branch CNN that reduces computational complexity while maintaining high segmentation accuracy on fine-resolution remote sensing images.

Findings

Achieves lower computational costs compared to state-of-the-art methods.

Maintains competitive segmentation accuracy on high-resolution remote sensing images.

Provides an open-source implementation for practical use.

Abstract

Semantic segmentation of remotely sensed images plays a crucial role in precision agriculture, environmental protection, and economic assessment. In recent years, substantial fine-resolution remote sensing images are available for semantic segmentation. However, due to the complicated information caused by the increased spatial resolution, state-of-the-art deep learning algorithms normally utilize complex network architectures for segmentation, which usually incurs high computational complexity. Specifically, the high-caliber performance of the convolutional neural network (CNN) heavily relies on fine-grained spatial details (fine resolution) and sufficient contextual information (large receptive fields), both of which trigger high computational costs. This crucially impedes their practicability and availability in real-world scenarios that require real-time processing. In this paper, we propose an Attentive Bilateral Contextual Network (ABCNet), a convolutional neural network (CNN) with double branches, with prominently lower computational consumptions compared to the cutting-edge algorithms, while maintaining a competitive accuracy. Code is available at https://github.com/lironui/ABCNet.