协同多时相波谱特征的不透水面信息级联提取

doi:10.12082/dqxxkx.2021.200236

Abstract

Abstract:

Impervious surface acts as an important technical indicator of urban characterization and regional urbanization dynamics. Its location, patch size, and spatial distribution are widely used by the communities of surface hydrothermal cycle and energy balance. However, traditional methods usually adopt a single time-phase image to retrieval the impervious surface information without considering the rich information implied in multiple time-phase images. Therefore, this paper presented a method to extract the impervious surface by integrating features in time-series images within a year. We generated the Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI) from time-series Landsat-8 OLI surface reflectance images to analyze the intra-annual features of multi-temporal spectrum curves between typical objects and summarize principles in describing changes of impervious surface from multiple time phases. We also combined the prior knowledge of effective impervious surface information to improve the extraction accuracy. Finally, we used field survey data and the 30-m impervious map generated from the 2-m GF-1 image by screen digitalization to validate the accuracy of results using three combinations of images as input (i.e., single time-phase image, four images of seasons, and multi-temporal images). The results show that the impervious surface extracted from the single-phase image had the lowest accuracy, while the extraction accuracy using multi-temporal images was the highest with an overall accuracy of 93.66% and a Kappa coefficient of 0.81. The extraction accuracy using the four images of seasons was in the middle. Furthermore, the presented method showed potential advantages of effectively identifying impervious surfaces in rural areas. Our method provides a new idea for the impervious surface extraction through integrating temporal spectral features of impervious surface.

Key words: time-series features, impervious surface, prior knowledge, cascade retrieval, accuracy comparison, Landsat OLI, spectrum characteristic, remote sensing index

SHUAI Yanmin, MA Xianwei, QU Ge, SHAO Congying, LIU Tao, LIU Shoumin, HUANG Huabing, GU Lingxiao, LATIPA Tuerhanjiang, LIANG Ji, LI Ling. Cascade Extraction of Impervious Surface Information based on the Signature of Temporal Spectrum[J].Journal of Geo-information Science, 2021, 23(1): 171-186.DOI:10.12082/dqxxkx.2021.200236

Figures/Tables 14

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References 41

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地物类型	反射率样本均值
地物类型	Blue	Green	Red	NIR	SWIR1	SWIR2
水库	0.0664	0.0961	0.0687	0.0727	0.0520	0.0399
城市河流	0.0523	0.0720	0.0658	0.0766	0.0600	0.0481
黄河	0.0740	0.1265	0.1585	0.1357	0.0281	0.0200
冬播耕地	0.1015	0.1450	0.1826	0.2707	0.2898	0.2645
城市灌木丛	0.0966	0.1354	0.1662	0.2568	0.2731	0.2355
春播耕地	0.0384	0.0635	0.0563	0.3736	0.1575	0.0888
人工草坪	0.0721	0.1089	0.1130	0.2566	0.1918	0.1431
沥青道路	0.0783	0.1070	0.1189	0.2019	0.1808	0.1561
水泥道路	0.1431	0.1868	0.2128	0.2700	0.2651	0.2414
沥青屋顶	0.0598	0.0935	0.1009	0.1634	0.1523	0.1148
水泥屋顶	0.1562	0.2092	0.2387	0.2962	0.2952	0.2706
彩钢屋顶	0.1357	0.1095	0.1071	0.2585	0.3513	0.3251
高密度建筑区	0.1104	0.1508	0.1726	0.2268	0.2205	0.1960
低密度建筑区	0.1124	0.1539	0.1784	0.2520	0.2578	0.2356

	单时相		像元总数/个	使用者精度/%
	不透水面/个	非不透水面/个	像元总数/个	使用者精度/%
不透水面	320 889	160 740	481 629	66.63
非不透水面	25 451	1 077 053	1 102 504	97.69
像元总数	346 340	1 237 793	1 584 133
生产者精度/%	87.01	92.62
				总体精度/%	88.25
				Kappa系数	0.69
	四季相		像元总数/个	使用者精度/%
	不透水面/个	非不透水面/个	像元总数/个	使用者精度/%
不透水面	273 660	38 033	311 693	87.80
非不透水面	72 680	1 199 760	1 272 440	96.15
像元总数	346 340	1 237 793	1 584 133
生产者精度/%	79.01	96.93
				总体精度/%	93.01
				Kappa系数	0.79
	多时相		像元总数/个	使用者精度/%
	不透水面/个	非不透水面/个	像元总数/个	使用者精度/%
不透水面	284 424	38 559	322 983	88.06
非不透水面	61 916	1 199 234	1 261 150	95.09
像元总数	346 340	1 237 793	1 584 133
生产者精度/%	82.12	96.88
				总体精度/%	93.66
				Kappa系数	0.81

Cascade Extraction of Impervious Surface Information based on the Signature of Temporal Spectrum

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