基于空间光谱信息协同的城市不透水层提取方法比较研究

doi:10.12302/j.issn.1000-2006.201911012

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南京林业大学学报（自然科学版） ›› 2021, Vol. 45 ›› Issue (1): 212-218.doi: 10.12302/j.issn.1000-2006.201911012

基于空间光谱信息协同的城市不透水层提取方法比较研究

范佳辉¹(), 张亚丽¹, 李明诗¹^,²^,^*()

1.南京林业大学林学院,江苏南京 210037
2.南京林业大学,南方现代林业协同创新中心,江苏南京 210037

收稿日期:2019-11-06 接受日期:2020-03-15 出版日期:2021-01-30 发布日期:2021-02-01
通讯作者: 李明诗
基金资助:
国家自然科学基金项目(31971577);国家自然科学基金项目(31670552);江苏省青蓝工程项目(2017)

Comparing four methods for extracting impervious surfaces using spectral information in synergy with spatial heterogeneity of remotely sensed imagery

FAN Jiahui¹(), ZHANG Yali¹, LI Mingshi¹^,²^,^*()

1. College of Forestry, Nanjing Forestry University, Nanjing 210037,China
2. Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037,China

Received:2019-11-06 Accepted:2020-03-15 Online:2021-01-30 Published:2021-02-01
Contact: LI Mingshi

摘要/Abstract

摘要：

【目的】利用不同的端元提取方法及混合像元分解算法计算南京市2018年城市不透水层覆盖度,评估各方法的精度,为城市可持续发展提供可靠的基础数据支撑。【方法】采用Landsat 8 OLI遥感影像,基于像元纯度指数(pixel purity index,PPI)并考虑空间光谱信息协同提出空间像元纯度指数(spatial pixel purity index,SPPI),精炼提纯植被、裸土、高反照度不透水层及低反照度不透水层4种类型端元,利用线性混合光谱模型(linear mixed spectral model,LMM)、混合调制匹配滤波(mixture tuned matched filtering,MTMF)、双线性混合光谱模型(bilinear mixed spectral model,BMM)及BP神经网络(BP neural network,BPNN)算法提取南京城市不透水层,采用同年的Google Earth遥感影像目视解译结果对提取的不透水层丰度进行精度验证。【结果】SPPI能有效结合多光谱波段的光谱信息和全色波段的空间信息,提高端元提取精度并减少计算量;同时,基于SPPI的BP神经网络算法提取精度最高,为90.45%;而基于PPI的线性混合光谱模型精度最低,为80.62%。BP神经网络算法在复杂城市中的解混精度高于线性混合光谱模型、混合调制匹配滤波和双线性混合光谱模型。【结论】采用全色波段像元亮度空间异质性辅助提取端元的方法,用空间信息弥补多光谱波段光谱信息较少的缺点,对于改进或发展适用于中/高分辨率多光谱影像的端元提取方法具有一定的参考价值,将其与神经网络模型结合可以在城市不透水层提取中推广应用。

关键词: 城市不透水层, 空间像元纯度指数, 混合调制匹配滤波, 双线性混合光谱模型, BP神经网络, 线性混合光谱模型

Abstract:

【Objective】 In order to provide a reliable foundation for the sustainable development of Nanjing City, this study compared different endmember extraction methods and mixed pixel decomposition algorithms to calculate the coverage of urban impervious surfaces (UISs) in Nanjing in 2018, and evaluated the accuracy of each method. 【Method】 Using Landsat 8 OLI images, an improved spatial pixel purity index (SPPI), based on the pixel purity index (PPI), was first proposed to refine or purify the endmembers for four land cover types including vegetation, bare soil, high albedo UISs and low albedo UISs. Next, based on the two extracted suites of endmembers derived from PPI and SPPI, a linear spectral mixed model, a mixture tuned matched filtering, a bilinear mixed spectral model and a BP neural network were implemented to extract UISs in Nanjing. The extracted UIS abundance was validated using visual interpretation of high spatial resolution Google Earth images for 2018. 【Result】 The results showed that SPPI could improve the identification accuracy of endmembers effectively and reduce the computational load by combining the spectral information from multi-spectral bands with the spatial heterogeneity of the panchromatic band. The UIS extraction accuracy of the SPPI-based BP network algorithm was the highest, at 90.45%, while the PPI-based linear spectral mixed model was the lowest at 80.62%. Overall, the BP network model outperformed the other three models in UIS extraction, regardless of whether PPI-based or SPPI-based endmembers were used. 【Conclusion】 The proposed method of using the panchromatic band to assist with extracting endmembers effectively integrated spatial heterogeneity to compensate for inadequate spectral information in the multispectral bands. The study provides methodological references to improve or develop means for endmember extraction from medium/high-resolution multispectral images. Additionally, integration of the SPPI and BP network model can be popularized in the UIS extraction domain.

Key words: urban impervious surface, spatial pixel purity index(SPPI), mixture tuned matched filtering(MTMF), bilinear mixed spectral model(BMM), BP neural network(BPNN), linear mixing spectral model(LMM)

中图分类号:

P237

范佳辉,张亚丽,李明诗. 基于空间光谱信息协同的城市不透水层提取方法比较研究[J]. 南京林业大学学报（自然科学版）, 2021, 45(1): 212-218.

FAN Jiahui, ZHANG Yali, LI Mingshi. Comparing four methods for extracting impervious surfaces using spectral information in synergy with spatial heterogeneity of remotely sensed imagery[J].Journal of Nanjing Forestry University (Natural Science Edition), 2021, 45(1): 212-218.DOI: 10.12302/j.issn.1000-2006.201911012.

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点号 No.	Google Earth	PPI_LSMM	PPI_MTMF	PPI_BMM	PPI_BPNN	SPPI_LSMM	SPPI_MTMF	SPPI_BMM	SPPI_BPNN
1	0.126 5	0.141 0	0.331 7	0.237 1	0.225 0	0.297 1	0.303 2	0.213 8	0.224 6
2	0.365 5	0.287 1	0.262 7	0.389 8	0.316 0	0.297 8	0.257 3	0.396 7	0.329 6
3	0.475 7	0.309 1	0.421 6	0.546 8	0.449 6	0.587 4	0.402 3	0.587 1	0.445 7
…	…	…	…	…	…	…	…	…	…
49	0.216 1	0.106 8	0.176 3	0.149 5	0.186 2	0.116 4	0.177 1	0.156 4	0.186 4
50	0.225 8	0.321 2	0.322 4	0.331 7	0.281 4	0.329 8	0.285 2	0.321 7	0.291 8
窗口平均精度/% average accuracy		80.62	83.17	87.56	88.37	81.37	86.42	89.50	90.45

基于空间光谱信息协同的城市不透水层提取方法比较研究

Comparing four methods for extracting impervious surfaces using spectral information in synergy with spatial heterogeneity of remotely sensed imagery

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