Enhanced Automated Quality Assessment Network for Interactive Building Segmentation in High-Resolution Remote Sensing Imagery
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914645227339776 |
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| author | Zhang, Zhili Hu, Xiangyun Xu, Jiabo |
| author_facet | Zhang, Zhili Hu, Xiangyun Xu, Jiabo |
| contents | In this research, we introduce the enhanced automated quality assessment network (IBS-AQSNet), an innovative solution for assessing the quality of interactive building segmentation within high-resolution remote sensing imagery. This is a new challenge in segmentation quality assessment, and our proposed IBS-AQSNet allievate this by identifying missed and mistaken segment areas. First of all, to acquire robust image features, our method combines a robust, pre-trained backbone with a lightweight counterpart for comprehensive feature extraction from imagery and segmentation results. These features are then fused through a simple combination of concatenation, convolution layers, and residual connections. Additionally, ISR-AQSNet incorporates a multi-scale differential quality assessment decoder, proficient in pinpointing areas where segmentation result is either missed or mistaken. Experiments on a newly-built EVLab-BGZ dataset, which includes over 39,198 buildings, demonstrate the superiority of the proposed method in automating segmentation quality assessment, thereby setting a new benchmark in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhanced Automated Quality Assessment Network for Interactive Building Segmentation in High-Resolution Remote Sensing Imagery Zhang, Zhili Hu, Xiangyun Xu, Jiabo Computer Vision and Pattern Recognition Human-Computer Interaction In this research, we introduce the enhanced automated quality assessment network (IBS-AQSNet), an innovative solution for assessing the quality of interactive building segmentation within high-resolution remote sensing imagery. This is a new challenge in segmentation quality assessment, and our proposed IBS-AQSNet allievate this by identifying missed and mistaken segment areas. First of all, to acquire robust image features, our method combines a robust, pre-trained backbone with a lightweight counterpart for comprehensive feature extraction from imagery and segmentation results. These features are then fused through a simple combination of concatenation, convolution layers, and residual connections. Additionally, ISR-AQSNet incorporates a multi-scale differential quality assessment decoder, proficient in pinpointing areas where segmentation result is either missed or mistaken. Experiments on a newly-built EVLab-BGZ dataset, which includes over 39,198 buildings, demonstrate the superiority of the proposed method in automating segmentation quality assessment, thereby setting a new benchmark in the field. |
| title | Enhanced Automated Quality Assessment Network for Interactive Building Segmentation in High-Resolution Remote Sensing Imagery |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2401.09828 |