Land cover classification of synthetic aperture radar images based on encoder--decoder network with an attention mechanism

Zheng, Nai-Rong; Zian, Yang; Shi, Xianzheng; Zhou, Ruoyi; Wang, Rui

doi:10.1117/1.jrs.16.014520

Cited by 3 publications

(4 citation statements)

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“…Researchers find it very challenging classifying SAR data and the segmentation is poorly understood [88]. Some studies have undertaken land cover classification and segmentation tasks across diverse categories of SAR data [91], including polarimetric SAR imagery [78,88,90], single-polarization SAR images [92], and multi-temporal SAR data [83]. As consideration, we recommend a roadmap for simplified and automated semantic segmentation of SAR images should be investigated.…”

Section: Analysis Of Rs Imagesmentioning

confidence: 99%

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A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

Ajibola,

Cabral

2024

Preprint

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Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen &amp; Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

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Section: Analysis Of Rs Imagesmentioning

confidence: 99%

“…Similarly, the High-Resolution GaoFen-3 SAR Dataset is useful for the Semantic Segmentation of Building [34,89,90]. The benchmark dataset Gaofen-3 (GF-3), comprised of single-polarization SAR images, holds significant importance [91]. This dataset is derived from China's pioneering civilian C-band polarimetric SAR satellite, designed for high-resolution RS.…”

mentioning

confidence: 99%

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

Ajibola,

Cabral

2024

Preprint

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“…Similarly, the High-Resolution GaoFen-3 SAR Dataset is useful for the Semantic Segmentation of Building [34,79,85]. The benchmark dataset Gaofen-3 (GF-3), comprised of single-polarization SAR images, holds significant importance [86]. This dataset is derived from China's pioneering civilian C-band polarimetric SAR satellite, designed for high-resolution RS.…”

Section: • Data Sourcesmentioning

confidence: 99%

“…Researchers find it very challenging classifying SAR data and the segmentation is poorly understood [84]. Some studies have undertaken land cover classification and segmentation tasks across diverse categories of SAR data [86], including polarimetric SAR imagery [77,84,85], single-polarization SAR images [87], and multi-temporal SAR data [81]. As a consideration, we recommend that a roadmap for simplified and automated semantic segmentation of SAR images should be investigated.…”

Section: • Analysis Of Rs Imagesmentioning

confidence: 99%

A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping

Ajibola,

Cabral

2024

Remote Sensing

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Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by the USA (11.76%), France (5.88%), Spain (4%), and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most used data sources. Benchmark datasets such as ISPRS Vaihingen and Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder–decoder and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

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Land cover classification algorithm based on multi-modal collaboration and boundary-guided fusion

Zhang,

Wang,

Zhang

et al. 2024

J. Appl. Rem. Sens.

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The semantic imbalance of class boundary areas is a key factor in decreasing the classification accuracy of the remote sensing land cover algorithm. We propose a multi-source remote sensing image semantic segmentation network based on multi-modal collaboration and boundary-guided fusion (BGF). The BGF module uses class boundary information as a restriction condition, embeds semantic alignment strategies into the encoder, and enhances the deep semantic features of each mode. On this basis, the boundary guidance strategy is used to assign different weights to the boundary and the internal area of the category to guide the feature fusion. Furthermore, to reduce the impact of multi-modal feature heterogeneity on feature fusion, a cross-modal collaborative fusion module is constructed to associate complementary information between multi-modal features and fully explore the collaborative relationship between multi-modal images from both spatial and channel domains. The comparative experiments were conducted with representative algorithms on the WHU-OPT-SAR data set. The experimental results show that the proposed method has increased the mean intersection over union and overall accuracy indicators by 3.3% and 2.2%, respectively, compared with MCANet, especially that the road category intersection and merger ratio has increased by 10.0% compared with MCANet. We proved the effectiveness of the proposed model.

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Land cover classification of synthetic aperture radar images based on encoder--decoder network with an attention mechanism

Cited by 3 publications

References 0 publications

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping

Land cover classification algorithm based on multi-modal collaboration and boundary-guided fusion

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