顾及边缘的多时相SAR图像半监督建筑区提取

陈帅霖; 杨文; 李恒超; TAPETEDeodato; BALZTimo

doi:10.3969/j.issn.0258-2724.20220807

顾及边缘的多时相SAR图像半监督建筑区提取

doi: 10.3969/j.issn.0258-2724.20220807

1.
武汉大学电子信息学院，湖北武汉 430072
2.
西南交通大学信息科学与技术学院，四川成都 611756
3.
意大利航天局（ASI），罗马 00133
4.
武汉大学测绘遥感信息工程国家重点实验室，湖北武汉 430079

基金项目: 国家自然科学基金项目（61771351）

详细信息

作者简介:
陈帅霖（1997—），男，硕士，研究方向为遥感图像分割，E-mail：slchen1997@whu.edu.cn

通讯作者:
杨文（1976—），男，教授，研究方向为图像处理与计算机视觉，E-mail：yangwen@whu.edu.cn

中图分类号: TP751；P237
计量
- 文章访问数: 159
- HTML全文浏览量: 56
- PDF下载量: 29
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-21
- 修回日期: 2023-03-16
- 网络出版日期: 2024-06-26
- 刊出日期: 2023-03-24

Edge-Aware Semi-Supervised Built-up Area Extraction Using Multi-Temporal Synthetic Aperture Radar Images

1.
Electronic Information School, Wuhan University, Wuhan 430072, China
2.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
3.
Agenzia Spaziale Italiana (ASI), Rome 00133, Italy
4.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

摘要

摘要:
针对合成孔径雷达（synthetic aperture radar，SAR）图像中建筑区域难以辨识与标注的问题，提出一种结合改进的伪标签技术和边缘增强策略的半监督建筑区提取新方法. 首先，引入同一位置、不同时相的SAR图像作为自然数据增强手段，并通过多个不同时相图像的预测结果投票确定伪标签；其次，设计一种边缘增强辅助模块，通过特征图变形以修正建筑区主体特征，辅以跳跃连接改进边缘特征，并针对主体和边缘特征进行分离式监督；此外，构建一个包含2种传感器和2个城市区域的多时相SAR图像建筑区提取数据集，含1000幅带标注图像和800组无标注时序图像，并基于该数据集进行实验验证. 实验表明，在所构建测试集上，基线方法使用全量数据训练后交并比（intersection over union, IoU）为63.43%，而所提方法在使用10%和全量数据时IoU分别为63.46%和68.24%，仅利用10%的标注数据即可达到基线方法使用全量标注数据训练的精度.
- 建筑区提取 /
- 合成孔径雷达 /
- 半监督学习 /
- 边缘提取
Abstract:
To address the challenges of identifying and annotating built-up areas in synthetic aperture radar (SAR) images, a novel semi-supervised method for extracting built-up areas that combined improved pseudo-labeling techniques with an edge enhancement strategy was proposed. Initially, SAR images from the same location but at different time were introduced as a natural data augmentation method, and the pseudo-labels were determined by voting based on the prediction results of multi-temporal images. Subsequently, an edge-enhancement auxiliary module was designed, which corrected the body features of the built-up areas through feature map warping and improved edge features with skip connections. Separate supervision for the body and edge features was performed. Moreover, a dataset for extracting built-up areas in multi-temporal SAR images, which included two types of sensors and two urban areas, was constructed. This dataset contains 1,000 annotated images and 800 groups of unlabeled temporal images. Experimental validations based on this dataset have demonstrated that on the constructed test set, the baseline method trained with full data achieves an intersection over union (IoU) of 63.43%, while the proposed method reaches an IoU of 63.46% and 68.24% when using 10% and full data, respectively. Remarkably, using only 10% of the annotated data, the proposed method can achieve the precision that the baseline method has obtained with full annotated data.
- built-up area extraction /
- synthetic aperture radar /
- semi-supervised learning /
- edge extraction

HTML全文

图 1 MTSBED数据集覆盖的区域

Figure 1. Area covered by MTSBED dataset

下载: 全尺寸图片幻灯片

图 2 有标注图像示例

Figure 2. Annotated images

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图 3 无标注图像示例

Figure 3. Unlabeled images

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图 4 多时相伪标签网络流程

Figure 4. Process of multi-temporal pseudo-labeling network

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图 5 边缘增强模块流程

Figure 5. Process of edge enhancement module

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图 6 边缘增强模块结构

Figure 6. Structure of edge enhancement module

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图 7 不同方法的可视化结果对比

Figure 7. Comparison of visualization results of different methods

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图 8 边缘增强模块效果比较

Figure 8. Comparison of the results of edge enhancement module

下载: 全尺寸图片幻灯片

表 1 MTSBED数据集介绍

Table 1. Introduction of MTSBED dataset

参数	深圳地区	武汉地区
传感器	TerraSAR-X	COSMO-SkyMed
成像模式	条带	条带
入射角/（°）	35～39	20～25
分辨率/m	3	3
拍摄时间	2008 年 10 月— 2009 年 3 月	2011 年 5 月— 2020 年 11 月
时间分辨率/d	11～22	4～36
景数/张	9	13
注：这里的时间分辨率为MTSBED所包含数据的时间分辨率，不是SAR卫星重访的时间分辨率.

下载: 导出CSV

表 2 不同方法在MTSBED上的IoU性能比较

Table 2. Comparison of IoU performance of different methods on MTSBED %

有标注样本比例/%	仅标注样本	伪标签^[24]	CPS^[29]	FOCT^[30]	语义均衡^[31]	多时相伪标签 + 边缘增强
5	56.37	60.19	27.06	56.59	60.38	61.82
10	57.68	61.82	35.34	61.79	60.75	63.46
20	58.16	63.21	51.8	63.97	62.56	64.69
50	61.50	64.68	64.73	66.13	64.67	66.17
100	63.43	66.41	66.79	67.76	66.43	68.24

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表 3 边缘增强模块消融实验（IoU指标）

Table 3. Ablation experiment of edge enhancement module (IoU) %

有标注样本比例/%	仅标注样本	标注样本 + 边缘增强	多时相伪标签	多时相伪标签 + 边缘增强
10	57.68	59.56	63.43	63.46
20	58.16	61.02	63.82	64.69
50	61.50	62.82	65.13	66.17
100	63.43	67.54	67.01	68.24

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表 4 不同预测概率阈值对结果的影响（IoU指标）

Table 4. Effect of different prediction probability thresholds on results (IoU) %

项目	0.85	0.90	0.95	0.99
伪标签	63.15	64.02	64.68	63.94
多时相伪标签	64.58	65.13	64.84	63.71

下载: 导出CSV

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