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

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.

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• Building extraction with vision transformer
• A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
• Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network
• A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation
• Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study
• MKANet: A Lightweight Network with Sobel Boundary Loss for Efficient Land-cover Classification of Satellite Remote Sensing Imagery
• SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image
• Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers
• MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images
• Graph Information Bottleneck for Remote Sensing Segmentation
• AeroReformer: Aerial Referring Transformer for UAV-based Referring Image Segmentation
• ClassWise-CRF: Category-Specific Fusion for Enhanced Semantic Segmentation of Remote Sensing Imagery