Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images

By Shunyi Zheng, Chenxi Duan, Jianlin Su. 2020

578 citations recorded by the source corpus.

Semantic segmentation of remote sensing images plays an important role in a wide range of applications, including land resource management, biosphere monitoring, and urban planning. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks, several limitations exist in standard models. First, for encoder–decoder architectures such as U-Net, the utilization of multiscale features causes the underuse of information, where low-level features and high-level features are concatenated directly without any refinement. Second, long-range dependencies of feature maps are insufficiently explored, resulting in suboptimal feature representations associated with each semantic class. Third, even though the dot-product attention mechanism has been introduced and utilized in semantic segmentation to model long-range dependencies, the large time and space demands of attention impede the actual usage of attention in application scenarios with large-scale input. This article proposed a multiattention network (MANet) to address these issues by extracting contextual dependencies through multiple efficient attention modules. A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention. Based on kernel attention and channel attention, we integrate local feature maps extracted by ResNet-50 with their corresponding global dependencies and reweight interdependent channel maps adaptively. Numerical experiments on two large-scale fine-resolution remote sensing datasets demonstrate the superior performance of the proposed MANet. Code is available at https://github.com/lironui/Multi-Attention-Network.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, background): “without considering the background class, similarly to [26].”
A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images (2021, methodology): “The former aiming to enlarge the receptive fields using large kernel sizes [13], dilated convolutions [16], or feature pyramids [2], whereas the latter [4, 17, 18] focuses on integrating attention mechanisms [20] with the FCN architecture to capture long-range dependencies of the feature maps.”
CMTFNet: CNN and Multiscale Transformer Fusion Network for Remote-Sensing Image Semantic Segmentation (2023, background): “[37] proposed a multiattention network (MANet) to extract contextual dependencies through multiple efficient attention modules.”
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery (2021, unknown): “(Li et al., 2021c) but also outperform recent Transformer-based lightweight networks, such as Segmenter (Strudel et al., 2021) and BANet (Wang et al., 2021b).”