基于Cubist模型树的城市不透水面百分比遥感估算模型

doi:10.3724/SP.J.1047.2016.01399

摘要/Abstract

摘要：

不透水面是城市区域中一种典型的土地覆盖类型,是衡量城市环境质量和城市化水平的重要标志之一。与传统基于像元级的遥感研究方法相比,不透水面百分比（Impervious Surface Percent,ISP）的估算可以进入像元内部,获得更准确的城市信息。本文应用Cubist模型树,对Landsat TM的原始波段变量（除热红外波段）,建立ISP估算的基础模型（Base Cubist-ISP）。通过基于模型树的集成学习优化算法和加入相邻时相影像的波段变量中值,以削弱噪声的影响。然后,优选热红外波段和各种衍生变量,并进行属性精简,继而应用集成学习算法得到的参数和精简后的变量建立ISP估算的优化模型（Optimal Cubist-ISP）。对广东省广州市海珠区的实验结果表明,Optimal Cubist-ISP模型估算不透水面的整体均方根误差（RMSE）为12.98%,决定系数（R²）为0.90,精度明显优于Base Cubist-ISP模型,RMSE降低约5.03%,ISP在透水面区域被高估和高密度不透水面区域被低估的现象得到改善。本文提出的基于Cubist模型树建立ISP遥感估算的模型及优化方法可以适用于城市区ISP的提取。

关键词: 不透水面, Cubist模型树, 集成学习算法, Optimal Cubist-ISP模型

Abstract:

As a typical land-cover type, impervious surface is a key indicator of urban environmental quality and urbanization scope. In comparison with the traditional remote sensing image processing methods, the assessment of impervious surface percentage (ISP) can offer the sub-pixel level exploration and acquire the fine-scale information. In this paper, the proposed method uses the Cubist model tree with both the high-resolution (Google Earth) and the medium-resolution (Landsat TM/ETM+) remote sensing data to establish an estimation model of impervious surface percentage (ISP). A base model (Base Cubist-ISP) is built integrating all the original bands from Landsat TM excluding the thermal infrared band. This paper tries to minimize the effects of noise by adopting the ensemble learning algorithm and by incorporating the median of each solar-reflective band within the adjacent temporal images. After that, the following variables are filtered to get the optimized results, including the TM thermal infrared band, the derived variables from the original bands such as Texture, and the tasseled cap transformation variables. Then the variables are simplified, and in that way, the optimized parameter of ensemble learning algorithm for Cubist tree and the well-chosen variables are used to establish an optimization estimation model (Optimal Cubist-ISP). The results of a case study for Haizhu district, which is located in Guangzhou city of Guangdong Province, show that the overall root mean square error between the estimated ISP value, which is based on the Optimal Cubist-ISP model, and the reference ISP value is 12.98%, with a determinant coefficient of 0.90. Moreover, this paper compares the Base Cubist-ISP model with the Optimal Cubist-ISP model. The accuracy of the Optimal Cubist-ISP model is better than the Base Cubist-ISP model, and the RMSE decreases by about 5.03%. It is illustrated that the Base Cubist-ISP model may over-estimate the pervious surface area and under-estimate the high density impervious surface area, which could be improved by the model optimization. In addition, the Optimal Cubist-ISP model can not only be able to well recognize the land types of soil and water, but also eliminate the influence of shadow on the high density building area to a certain extent. Thus, the proposed approach on impervious surface estimation based on the Cubist model tree as well as its optimization scheme can be applied for precisely obtaining the ISP in the urban areas.

Key words: impervious surface, Cubist model tree, ensemble learning algorithm, Optimal Cubist-ISP model

戴舒, 付迎春, 赵耀龙. 基于Cubist模型树的城市不透水面百分比遥感估算模型[J]. 地球信息科学学报, 2016, 18(10): 1399-1409.DOI:10.3724/SP.J.1047.2016.01399

DAI Shu,FU Yingchun,ZHAO Yaolong. The Remote Sensing Model for Estimating Urban Impervious Surface Percentage Based on the Cubist Model Tree[J]. Journal of Geo-information Science, 2016, 18(10): 1399-1409.DOI:10.3724/SP.J.1047.2016.01399

图/表 17

图1

图2

图3

图4

表1

表2

表3

表4

表5

图5

表6

图6

图7

图8

表7

表8

图9

参考文献 29

[1]	Yuan F, Bauer M E.Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery[J]. Remote Sensing of Environment, 2007,106(3):375-386. doi: 10.1016/j.rse.2006.09.003
[2]	Arnold Jr C L, Gibbons C J. Impervious surface coverage: the emergence of a key environmental indicator[J]. Journal of the American Planning Association, 1996,62(2):243-258.
[3]	Weng Q.Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends[J]. Remote Sensing of Environment, 2012,117:34-49. doi: 10.1016/j.rse.2011.02.030
[4]	Yang X J.Estimating landscape imperviousness index from satellite imagery[J]. IEEE Geoscience & Remote Sensing Letters, 2006,3(1):6-9. doi: 10.1109/LGRS.2005.853929
[5]	Bauer M E, Loffelholz B C, Wilson B.Estimating and mapping impervious surface area by regression analysis of Landsat imagery[A]. In: Weng Q. Remote sensing of impervious surfaces[M]. Boca Rotan, FL: CRC Press, 2008:39-58.
[6]	Ridd M K.Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities[J]. International Journal of Remote Sensing, 1995,16(12):2165-2185. doi: 10.1080/01431169508954549
[7]	Wu C S, Murray A T.Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment, 2003,84(4):493-505. doi: 10.1016/S0034-4257(02)00136-0
[8]	Flanagan M, Civco D L.Subpixel impervious surface mapping[C]. Proceedings of ASPRS Annual Convention, 2001.
[9]	Hu X F, Weng Q.Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks[J]. Remote Sensing of Environment, 2009,113(10):2089-2102.
[10]	Xian G, Crane M.Assessments of urban growth in the Tampa Bay watershed using remote sensing data[J]. Remote Sensing of Environment, 2005,97(2):203-215. doi: 10.1016/j.rse.2005.04.017
[11]	廖明生,江利明,林珲,等.基于CART集成学习的城市不透水层百分比遥感估算[J].武汉大学学报·信息科学版,2007,32(12):1099-1102. doi: 10.3969/j.issn.1671-8860.2007.12.003
	[ Liao M, Jiang L, Lin H, et al.Estimating urban impervious surface percent using boosting as a refinement of CART analysis[J]. Geomatics and Information Science of Wuhan University, 2007,32(12):1099-1102. ] doi: 10.3969/j.issn.1671-8860.2007.12.003
[12]	朱艾莉,吕成文.城市不透水面遥感提取方法研究进展[J].安徽师范大学学报:自然科学版,2010,33(5):485-489. doi: 10.3969/j.issn.1001-2443.2010.05.019
	[ Zhu A, Lu C.Advances in the methods of extracting urban impervious surface based on remote sensing[J]. Journal of Anhui Normal University: Natural Science, 2010,33(5):485-489. ] doi: 10.3969/j.issn.1001-2443.2010.05.019
[13]	Quinlan J R.Learning with continuous classes[C]. Proceedings of Australian Joint Conference on Artificial Intelligence World Scientific, 1992:343-348.
[14]	高志宏,张路,李新延,等.城市土地利用变化的不透水面覆盖度检测方法[J].遥感学报,2010,14(3):593-606.
	[ Gao Z, Zhang L, Li X, et al.Detection and analysis of urban land use changes through multi-temporal impervious surface mapping[J]. Journal of Remote Sensing, 2010,14(3):593-606. ]
[15]	Yang L, Huang C Q, Wylie B K, et al.An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery[J]. Canadian Journal of Remote Sensing, 2003,29(2):230-240. doi: 10.5589/m02-098
[16]	Xian G.Mapping impervious surfaces using classification and regression tree algorithm[A]. In: Weng Q. Remote sensing of impervious surfaces[M]. Boca Rotan, FL: CRC Press, 2008:39-58.
[17]	Im J, Lu Z Y, Rhee J, et al.Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data[J]. Remote Sensing of Environment, 2012,117:102-113. doi: 10.1016/j.rse.2011.06.024
[18]	Walton J T.Subpixel urban land cover estimation: comparing Cubist, random forests, and support vector regression[J]. Photogrammetric Engineering & Remote Sensing, 2008,74:1213-1222. doi: 10.1007/s00190-008-0219-8
[19]	USGS. Landsat higher level science data products[ER/OL]. .
[20]	Schmidt G, Jenkerson C, Masek J, et al.Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description (No. 2013-1057)[R]. US Geological Survey, 2013.
[21]	Quinlan J R.Combining instance-based and model-based learning[C]. Machine Learning Proceedings, 1993:236-243.
[22]	Txomin H, Michael A W, Joanne C W, et al.An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites[J]. Remote Sensing of Environment, 2015,158:220-234.
[23]	Neil F.Seasonal composite Landsat TM/ETM+ images using the medoid (a multi-dimensional median)[J]. Remote Sensing, 2013,5(12):6481-6500. doi: 10.3390/rs5126481
[24]	Weng Q.A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China[J]. International Journal of Remote Sensing, 2001,22(10):1999-2014. doi: 10.1080/713860788
[25]	Price J C.Using spatial context in satellite data to infer regional scale evapotranspiration[J]. IEEE Transactions on Geoscience & Remote Sensing, 1990,28(5):940-948. doi: 10.1109/36.58983
[26]	Zha Y, Gao J, Ni S.Use of normalized difference built-up index in automatically mapping urban areas from TM imagery[J]. International Journal of Remote Sensing, 2003,24(3):583-594. doi: 10.1080/01431160304987
[27]	Zhao H M, Chen X L.Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+[J]. Geoscience & Remote Sensing Symposium Proceedings, 2005,3:1666-1668. doi: 10.1109/IGARSS.2005.1526319
[28]	Crist E P.A TM tasseled cap equivalent transformation for reflectance factor data[J]. Remote Sensing of Environment, 1985,17(85):301-306. doi: 10.1016/0034-4257(85)90102-6
[29]	徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报,2005,9(5):589-595. doi: 10.3321/j.issn:1007-4619.2005.05.012
	[ Xu H Q.A study on iInformation extraction of water body with the modified normalized difference water Index(MNDWI)[J]. Journal of Remote Sensing,2005,9(5):589-595. ] doi: 10.3321/j.issn:1007-4619.2005.05.012

committees	MBE/(%)	MAE/(%)	RMSE/(%)
1	-0.47	12.51	18.01
100	-0.25	12.19	17.50

实验	MAE/(%)	RMSE/(%)	输入的自变量
1	12.19	17.50	b1-6
2	10.92	16.20	b1-6_med
3	12.26	17.45	b1-6,b7
4	11.26	16.21	b1-6,su_b7

实验	MAE/(%)	RMSE/(%)	输入的自变量（opt_b7：b1-6_med,su_b7）
1	10.41	15.39	opt_b7
2	10.34	15.42	opt_b7,NDVI
3	9.82	14.45	opt_b7,NDVI_max
4	8.77	12.67	opt_b7,NDVI_max,NDBI,NDBaI,TC,Texture
5	9.04	13.21	opt_b7,NDBI,NDBaI,TC,Texture
6	8.74	12.67	opt_b7,NDVI_max,NDBaI,TC,Texture
7	8.76	12.82	opt_b7,NDVI_max,NDBI,TC,Texture
8	8.84	12.72	opt_b7,NDVI_max,NDBI,NDBaI,Texture
9	9.38	13.83	opt_b7,NDVI_max,NDBI,NDBaI,TC

实验	b1-6_med	MNF1-4_med	su_b7	NDVI_max	Texture	MAE(%)	RMSE(%)
1	√		√	√	√	8.84	12.86
2		√	√	√	√	8.94	13.09

属性变量	MNF2_med	NDVI_max	MNF1_med	SU_TM6	var	MNF3_med	mean
重要度	79	64	59	55.5	47.5	47	46
属性变量	MNF4_med	diss	contr	homog	correl	sec_mom	entropy
重要度	38	30	28.5	19	6.5	1	0