One Model Is Enough: Toward Multiclass Weakly Supervised Remote Sensing Image Semantic Segmentation

Li, Zhenshi; Zhang, Xueliang; Xiao, Pengfeng

doi:10.1109/tgrs.2023.3290242

Cited by 9 publications

(3 citation statements)

References 68 publications

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“…It is possible to explore the applicability of our model across data of varying quality or types during the process. Therefore, it could provide further insights to combine our idea with existing relevant studies in spectral variability and data noise [84], [85].…”

Section: ⅳ Discussionmentioning

confidence: 90%

A Cascaded Network With Coupled High-Low Frequency Features for Building Extraction

Chen,

Xiao,

Zhang

et al. 2024

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

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Accurately extracting buildings from highresolution remote sensing images is crucial for human productivity and livelihood in urban areas. Due to varying scales and indistinct boundaries of buildings, it is crucial to fully leverage the high-and low-frequency features in building extraction from remote sensing images. However, previous studies have solely relied on either lowor high-frequency features, leading to errors like omissions or internal holes in the detected buildings at various scales. Although some studies have considered the integration between both highand low-frequency features, they overlook the suitability of different network depths for extracting different frequency features. A novel network called Cascaded Inception Conv-Former Network (CICF-Net) is proposed in this study to solve these problems. It leverages the parallel combination of convolutional neural network and Transformer to efficiently extract high-and low-frequency features for building extraction. In the encoder, as the network depth grows, we gradually reduce the contribution of high-frequency branch and enhance the focus on low-frequency branch. Moreover, a cascaded fusion strategy is employed to extract and integrate multi-scale high-and lowfrequency features. Meanwhile, we propose gated convolution UperNet as the decoder, which utilizes recursive gated convolution to facilitate multi-level spatial interactions and better restoration of fine-grained spatial details for building segmentation. The proposed CICF-Net achieves competitive accuracies on three public benchmarks: Massachusetts Building Dataset, WHU Aerial Building Dataset, and Inria Aerial Image Labeling Dataset, with IoU of 75.17%, 91.45%, and 81.28%, respectively. This provides strong evidence of its effectiveness in building extraction, as it can accurately capture spatial details and context of buildings.

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Section: ⅳ Discussionmentioning

confidence: 90%

A Cascaded Network With Coupled High-Low Frequency Features for Building Extraction

Chen,

Xiao,

Zhang

et al. 2024

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

Self Cite

View full text Add to dashboard Cite

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“…Collaborative global-local network (GLNet) [36], progressive semantic segmentation network (MagNet) [45], integrating shallow and deep features network (ISDNet) [46], patch proposal network (PPN) [47], image segmentation via locality-aware contextual correlation network (LCC) [48], and one model is enough for image semantic segmentation (OME) [49]. second for (imperious surface and trees) subclasses.…”

Section: ) Segmentation Models Designed For Ultra-high Resolution Ima...mentioning

confidence: 99%

“…Publication mIoU (%) GLNet [36] CVPR19 71.60 PPN [47] AAAI20 71.90 MagNet-Fast [45] CVPR21 71.85 MagNet [45] CVPR21 72.96 LCC [48] ICCV21 73.50 ISDNet [46] CVPR22 73.30 OME [49] TGRS23 68.28 MCN 73.73…”

Section: Modelsmentioning

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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Key Feature Repairing Based on Self-Supervised for Remote Sensing Semantic Segmentation

Wang,

Li,

Huo

2024

IEEE Geosci. Remote Sensing Lett.

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One Model Is Enough: Toward Multiclass Weakly Supervised Remote Sensing Image Semantic Segmentation

Cited by 9 publications

References 68 publications

A Cascaded Network With Coupled High-Low Frequency Features for Building Extraction

A Cascaded Network With Coupled High-Low Frequency Features for Building Extraction

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

Key Feature Repairing Based on Self-Supervised for Remote Sensing Semantic Segmentation

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