基于高分辨率遥感影像深度特征的城市非渗透表面集成学习提取

doi:10.11947/j.AGCS.2024.20220389

摘要/Abstract

摘要：

城市非渗透表面信息的有效提取是高分辨率遥感应用研究的热点问题。针对目前城市非渗透表面信息提取中存在的问题，结合深度学习与集成学习的优势，提出了基于高分辨率遥感影像深度特征的城市非渗透表面集成学习方法。以高分二号多光谱数据为试验数据，以非渗透表面密集程度不同的城市区域为试验区，基于U-Net深度网络提取的高分辨率影像的深层次特征，采用Stacking机制的集成学习机提取城市非渗透表面信息。试验结果表明，基于深度特征的集成学习方法在城市非渗透表面信息提取中获得了较高的精度。在城市非渗透表面密集程度不同的试验区，总体精度不低于91.66%，Kappa系数不低于0.83；错分误差为7.83%~9.39%，漏分误差为7.22%~14.88%。相对于基于浅层光谱信息的集成学习、随机森林、支持向量机，总体精度、Kappa系数有显著提高，错分与漏分误差显著降低。说明深度特征信息能有效提高集成学习提取非渗透表面提取的用户精度与制图精度；相对于U-Net、SegNet深度学习网络，在稀疏、中等密集、密集、复杂4类非渗透表面试验区，有效提高了总体精度、Kappa系数，漏分、错分误差显著的减少。说明基于深度特征的集成学习能有效改善与提高非渗透表面提取的用户精度与制图精度。总体上，基于深度特征的高分辨率非渗透表面集成学习方法能获得较高的城市非渗透表面信息提取精度，在城市土地利用/覆盖分类中具有良好的应用前景。

关键词: U-Net网络, 集成学习, 深度特征, 城市非渗透表面, 高分辨率遥感, GF-2

Abstract:

The effective extraction of urban impervious surface is important on the application of high-resolution remote sensing. Focusing on existing issues, an urban impervious surface extraction method is proposed based on U-Net combining with ensemble machine learning. The impervious surface areas with different density are selected as the experimental areas. Firstly, the deep features of high-resolution images are extracted by U-Net with the GF-2 multispectral data. Then, the urban impervious surface is extracted by using the ensemble learning with stacking mechanism. The experimental results show that the ensemble learning based on deep features of high-resolution remote sensing image can obtain high accuracy of urban impervious surface extraction. In the experimental areas with different density of urban impervious surface, the overall accuracy is not less than 91.66%, and Kappa is not less than 0.83. The commission error is 7.83%~9.39%; the omission error is 7.22%~14.88%. Compared with the ensemble learning, random forest and support vector machine based on image spectral features, the overall accuracy and Kappa are increased in experimental areas with relatively sparse, medium dense, dense and complex distribution of impervious surface. The commission and omission errors are significantly reduced. This indicates that the deep features can effectively improve the mapping accuracy and user accuracy of integrated learning to extract impervious surface. Compared with U-Net and SegNet, the overall accuracy and Kappa are increased by in the four experimental areas with relatively sparse, medium dense, dense and complex distribution of impervious surface. The commission errors are significantly reduced. The integration ensemble learning with deep learning can effectively improve the mapping accuracy and user accuracy of impervious surface extraction. In general, the ensemble learning based on the deep features of high-resolution remote sensing image can obtain higher accuracy of urban impervious surface extraction, which has application prospects in urban land use/cover classification.

Key words: U-Net network, ensemble learning, deep features, urban impervious surface, high spatial resolution, GF-2

中图分类号:

P237

李雪涛, 王盼成, 曾永年. 基于高分辨率遥感影像深度特征的城市非渗透表面集成学习提取[J]. 测绘学报, 2024, 53(4): 700-711.

Xuetao LI, Pancheng WANG, Yongnian ZENG. Urban impervious surface extraction based on the deep features of high-resolution remote sensing image and ensemble learning[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 700-711.

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密集程度	ACC/(%)	P/(%)	R/(%)	F₁值	Kappa系数
稀疏	98.26	91.51	85.12	0.88	0.87
中等密集	97.29	92.17	88.16	0.90	0.89
密集	91.66	90.61	92.78	0.92	0.83
复杂	93.91	91.41	90.25	0.91	0.86

方法	密集程度	ACC/(%)	P/(%)	R/(%)	F₁值	Kappa系数
本文方法	稀疏	98.26	91.51	85.12	0.88	0.87
	中等密集	97.29	92.17	88.16	0.90	0.89
	密集	91.66	90.61	92.78	0.92	0.83
	复杂	93.91	91.41	90.25	0.91	0.86
集成学习	稀疏	96.32	74.85	78.00	0.76	0.74
	中等密集	92.41	68.98	83.20	0.76	0.71
	密集	78.92	93.09	62.06	0.78	0.58
	复杂	88.84	85.44	80.46	0.83	0.75
随机森林	稀疏	62.51	97.10	75.86	0.69	0.66
	中等密集	59.11	89.24	74.89	0.66	0.60
	密集	90.16	78.02	62.47	0.74	0.67
	复杂	73.15	85.92	91.72	0.81	0.70
支持向量机	稀疏	67.31	95.66	83.89	0.75	0.72
	中等密集	65.00	91.24	80.99	0.72	0.67
	密集	92.12	72.03	47.63	0.63	0.44
	复杂	67.86	83.30	95.40	0.79	0.66
U-Net	稀疏	97.62	91.39	75.96	0.84	0.82
	中等密集	95.74	90.99	77.23	0.844	0.81
	密集	89.85	89.75	87.32	0.90	0.80
	复杂	89.78	87.72	80.86	0.83	0.77
SegNet	稀疏	92.74	71.11	81.02	0.76	0.72
	中等密集	96.25	76.69	73.16	0.75	0.73
	密集	84.95	85.60	83.71	0.85	0.70
	复杂	88.53	85.47	79.30	0.82	0.74