Lightweight Attention Network for Very High-Resolution Image Semantic Segmentation

Guan, Renchu; Wang, Mingming; Bruzzone, Lorenzo; Zhao, Haishi; Yang, Chen

doi:10.1109/tgrs.2023.3272614

Cited by 15 publications

(1 citation statement)

References 44 publications

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“…2) CNN-based attentional networks: Lightweight attention network (LiANet) [41], segmentation network with spatial and channel attention (SCAttNet) [27], dual-channel scale-aware network with position and channel attention (DSPCANet) [29], attentive bilateral contextual network (ABCNet) [34], multiattention network (MANet) [33], and squeeze & excitation residual network (SERNet) [28].…”

Section: F Comparison Methodsmentioning

confidence: 99%

Multiscale Context-Aware Feature Fusion Network for Land-Cover Classification of Urban Scene Imagery

Siddique,

Li,

Azeem

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, several land cover classification models have achieved great success in terms of both accuracy and computational performance. However, it remains challenging due to inter-class similarities, intra-class variations, scale-related inaccuracies, and high computational complexity. First, these methods fail to establish a correlation among different feature maps during multiscale feature extraction, leading to inter-class similarities and intra-class variations. Second, they under-utilize feature interdependencies of the context contained in each layer of the encoder-decoder architecture, causing scale-related inaccuracies. Third, they cause checkerboard artifacts and blurry edges, which can negatively impact the accuracy and generated segmentation map at increased computational cost. To address these problems, this article proposes a novel multiscale contextaware feature fusion network (MCN) for high-resolution urban scene images. MCN mainly consists of three modules: 1) multiscale feature enhancement module (MFE) for backbone network to extract rich spatial information dynamically by incorporating dense correlation among feature maps with different receptive fields, 2) multilayer feature fusion module (MLF) as skip connections to produce a single high-level representation of the local-global context by capturing low-, mid-, high-level interdependencies at different encoder-decoder stages, and 3) pixel shuffle decoder (PSD) to reduce the blurry edges and checkerboard artifacts while upsampling with reduced number of parameters. Experiments on three high-resolution aerial and satellite urban scene datasets show that MCN consistently outperforms the mainstream land cover classification models. Specifically, MCN achieves an OA of 93.51 on Potsdam, 90.18 on Vaihingen, and a mIoU of 73.73 on DeepGlobe.

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Section: F Comparison Methodsmentioning

confidence: 99%

Multiscale Context-Aware Feature Fusion Network for Land-Cover Classification of Urban Scene Imagery

Siddique,

Li,

Azeem

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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CF‐Net: Cross fusion network for semantic segmentation

Wang,

Shen,

Dong

et al. 2024

IET Image Processing

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Semantic segmentation is a fundamental computer vision task, and deep learning methods have been successfully applied to this field. However, target morphology continues to exhibit the incomplete prediction problem, which is attributable to the low feature utilisation and the insufficiency of spatial location information. This paper proposes a novel cross fusion network with unit attention mechanism (CF‐Net) for semantic segmentation. The two hallmarks of the framework are the usage of a multi‐scale fusion module and the unit attention mechanism. Multi‐scale fusion module can integrate multi‐branch outputs with different receptive fields, which obtain fine‐grained target details and visual contextual information. The cross fusion network is optimised with a unit attention mechanism to fuse intermediate features, which enables the acquisition of more accurate and effective spatial location information while maintaining consistency in feature space. The experimental results demonstrate that the proposed CF‐Net outperforms favourably comparable with other existing methods on the CamVid, Cityscapes, and PASCAL VOC 2012 databases, which also verifies the Effectiveness and reliability of our method.

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Semantic Segmentation of Remotely Sensed Images using Multisource Data: An Experimental Analysis

Putty,

Annappa,

Prajwal

et al. 2024

2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Lightweight Attention Network for Very High-Resolution Image Semantic Segmentation

Cited by 15 publications

References 44 publications

Multiscale Context-Aware Feature Fusion Network for Land-Cover Classification of Urban Scene Imagery

Multiscale Context-Aware Feature Fusion Network for Land-Cover Classification of Urban Scene Imagery

CF‐Net: Cross fusion network for semantic segmentation

Semantic Segmentation of Remotely Sensed Images using Multisource Data: An Experimental Analysis

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