AOSN：α-最优网络模型的山区单通道SAR高程重建方法

doi:10.11947/j.AGCS.2025.20230431

测绘学报 ›› 2025, Vol. 54 ›› Issue (5): 888-898.doi: 10.11947/j.AGCS.2025.20230431

AOSN：α-最优网络模型的山区单通道SAR高程重建方法

罗卿莉¹(), 李雪岩¹, 黄国满², 陈红辉¹, 薛铭龙¹, 李健¹

^1.天津大学精密测试技术及仪器全国重点实验室，天津　300072
^2.中国测绘科学研究院，北京　100036

收稿日期:2024-09-27 修回日期:2025-04-08 出版日期:2025-06-23 发布日期:2025-06-23
作者简介:罗卿莉（1985—），女，博士，副教授，研究方向为SAR遥感与InSAR应用。E-mail：luoqingli@tju.edu.cn
基金资助:
天津市自然科学基金重点项目(21JCZDJC00670);国家自然科学基金(41601446)

AOSN: alpha optimal structure network for height estimation from a single SAR image in mountain areas

Qingli LUO¹(), Xueyan LI¹, Guoman HUANG², Honghui CHEN¹, Minglong XUE¹, Jian LI¹

^1.State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
^2.Chinese Academy of Surveying & Mapping, Beijing 100036, China

Received:2024-09-27 Revised:2025-04-08 Online:2025-06-23 Published:2025-06-23
About author:LUO Qingli (1985—), female, PhD, associate professor, majors in SAR remote sensing and InSAR applications.　E-mail: luoqingli@tju.edu.cn
Supported by:
Key Project of Tianjin Natural Science Foundation(21JCZDJC00670);The National Natural Science Foundation of China(41601446)

摘要/Abstract

摘要：

合成孔径雷达（SAR）高程重建技术是全天时、全天候、实时获取地表高程信息的一种有效途径。然而，单幅SAR图像反演地表高程是一个不适定问题，即一个相同的二维场景可能对应多个不同的三维场景。深度学习为解决上述问题提供了可能。现有的深度学习方法在山区单通道SAR高程重建时存在细节特征缺失和整体误差较大的问题。针对以上问题，本文提出了α-最优结构网络模型（AOSN）的山区单通道SAR高程重建方法。该方法通过引入结构参数α，寻找不同尺度卷积核的最优组合，同时引入了残差模块并使用转置卷积操作替代反池化操作，从而提高了网络模型的高程重建精度。贵阳、黄山、格尔木的试验结果表明，本文方法在山区相较于其他方法具有更高的高程重建精度。

关键词: SAR影像, 高程重建, 深度学习, 结构参数

Abstract:

Height estimation from a single synthetic aperture radar (SAR) image is a possible way to estimate height in all day and all weather conditions with real time capabilities. However, it is an ill-posed problem since the same 2D images may be projected from multiple 3D images. The development of deep learning provides a possible solution for it. The problems of the current deep learning methods are lack of detail feature information and the accuracy of the estimated height is not high enough. In order to address the above issue, this paper proposes alpha optimal structure network (AOSN), utilizing the characteristics of various feature extraction capabilities from the convolution kernels with different sizes. A structural parameter named α is proposed and it searches the optimal combination of convolution kernels with different sizes, and the residual block is introduced and the transposed convolution operation is used instead of the unpooling operation, which improves the final accuracy of height estimation. The experiments carried on the datasets on Guiyang, Huangshan, Geermu demonstrate that the proposed method outperforms the state-of-the-art in mountain areas.

Key words: SAR image, height estimation, deep learning, structural parameter

中图分类号:

P237

罗卿莉, 李雪岩, 黄国满, 陈红辉, 薛铭龙, 李健. AOSN：α-最优网络模型的山区单通道SAR高程重建方法[J]. 测绘学报, 2025, 54(5): 888-898.

Qingli LUO, Xueyan LI, Guoman HUANG, Honghui CHEN, Minglong XUE, Jian LI. AOSN: alpha optimal structure network for height estimation from a single SAR image in mountain areas[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(5): 888-898.

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模块	层	操作	输出尺寸
Input		拼接	（256，256，3）
	Down1	3×3Conv+5×5Conv+Pool	（128，128，64）
Down Block	Down2	3×3Conv+5×5Conv+Pool	（64，64，128）
	Down3	3×3Conv+5×5Conv+Pool	（32，32，256）
	[Conv1	Conv	（32，32，512）
5×[Down Resblocks]	Conv2	Conv	（32，32，512）
	Conv3］	Conv	（32，32，256）
	[Conv1	Conv	（32，32，512）
5×[Up Resblocks]	Conv2	Conv	（32，32，512）
	Conv3］	Conv	（32，32，512）
	Up1	3×3TransConv+5×5 TransConv+Bilinear	（32，32，256）
Up Block	Up2	3×3TransConv+5×5 TransConv+Bilinear	（64，64，128）
	Up3	3×3TransConv+5×5 TransConv+Bilinear	（128，128，64）
Output	Out	TransConv	（256，256，1）

数据集	残差模块和转置卷积模块	结构参数α	RMSE/m	MAE/m	SSIM
贵阳	N	N	43.18	29.63	0.33
	Y	N	36.72	24.51	0.47
	N	Y，Trainable	34.81	23.77	0.54
	Y	Y，Fixed	28.93	20.08	0.56
	Y	Y，Trainable	24.54	16.37	0.68
黄山	N	N	49.05	33.56	0.37
	Y	N	39.92	31.09	0.42
	N	Y，Trainable	39.47	30.26	0.47
	Y	Y，Fixed	30.10	27.64	0.49
	Y	Y，Trainable	26.29	21.64	0.61
格尔木	N	N	51.02	44.57	0.28
	Y	N	46.19	39.28	0.44
	N	Y，Trainable	44.16	35.80	0.52
	Y	Y，Fixed	36.69	32.94	0.56
	Y	Y，Trainable	29.19	24.16	0.58

数据集	网络	RMSE/m	MAE/m	SSIM
贵阳	PackNet	28.67	19.73	0.54
	VDSR	31.93	23.59	0.49
	AOSN	24.54	16.37	0.68
黄山	PackNet	29.48	23.19	0.57
	VDSR	34.38	26.11	0.52
	AOSN	26.29	21.64	0.61
格尔木	PackNet	35.58	28.53	0.51
	VDSR	41.51	34.89	0.42
	AOSN	29.19	24.16	0.58

数据集	RMSE/m	SSIM
格尔木测试集	29.19	0.58
黄山数据集	192.25	0.31

AOSN：α-最优网络模型的山区单通道SAR高程重建方法

AOSN: alpha optimal structure network for height estimation from a single SAR image in mountain areas

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