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CLC number: TP751.1

On-line Access: 2015-03-04

Received: 2014-03-07

Revision Accepted: 2014-05-30

Crosschecked: 2015-02-04

Cited: 2

Clicked: 2641

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Li-gang Ma

http://orcid.org/0000-0001-9377-2189

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.3 P.238-248

http://doi.org/10.1631/FITEE.1400083


Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model


Author(s):  Li-gang Ma, Jin-song Deng, Huai Yang, Yang Hong, Ke Wang

Affiliation(s):  Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   11114054@zju.edu.cn, jsong_deng@zju.edu.cn, kwang@zju.edu.cn

Key Words:  ZY-1 02C satellite, Classification, Urban, Multi-variable model


Li-gang Ma, Jin-song Deng, Huai Yang, Yang Hong, Ke Wang. Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(3): 238-248.

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Abstract: 
The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite (three bands), and the complexity and heterogeneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multi-variable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorrelation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy assessment which adopts traditional error matrices with stratified random samples and polygon area consistency (PAC) indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63% and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82% of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.

This is a useful manuscript comparing the metrics for landscape classification using multi-variables from the relatively new 02C satellite imagery. This is an important independent test for using new data, so that justifies the significance of the work. Overall the paper is clear and straightforward in its approach.

基于国产高分辨率遥感影像和面向对象多变量模型的城市土地利用分类

目的:资源一号02C星搭载国产遥感卫星序列中为数不多的高性能传感器之一,获取大量的影像数据。然而,在空间分辨率相对较高,光谱分辨率比较低的情况下,城市土地覆盖分类势必存在一定问题。如何深度挖掘影像光谱和空间信息,建立可行的技术方法流程,实现准确的城市土地覆盖分类,进而为其推广应用奠定基础十分必要。
创新点:提出光谱与空间领域信息、判别分析、面向对象法结合的技术流程体系(图2),实现城市土地覆盖的准确分类。对分类结果采用基于点和图斑面积的两种验证方法进行验证。
方法:计算图像纹理、空间自相关特征、形状指数、植被指数、不透水面含量等信息,与光谱信息结合,经过判别分析和相关分析的筛选,实现面向对象的分类和两种指标的精度评价。
结论:根据本文提出的技术路线,可以实现相对准确的城市土地覆盖分类。总体点位精度在92%以上(表2),面积精度达到82%以上,误差通常源自住宅和裸土的混淆。影像数据在城市土地覆盖分类方面非常有效。

关键词:02C星;分类;城市;多变量模型

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Adams, J.B., Sabol, D.E., Kapos, V., et al., 1995. Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sens. Environ., 52(2):137-154.

[2]Aguilar, M.A., Saldaña, M.M., Aguilar, F.J., 2013. GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. Int. J. Remote Sens., 34(7):2583-2606.

[3]Carlson, T.N., Ripley, D.A., 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ., 62(3):241-252.

[4]Chen, Y., Shi, P., Fung, T., et al., 2007. Object-oriented classification for urban land cover mapping with ASTER imagery. Int. J. Remote Sens., 28(20):4645-4651.

[5]Deng, J.S., Wang, K., Deng, Y.H., et al., 2008. PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int. J. Remote Sens., 29(16):4823-4838.

[6]Durieux, L., Lagabrielle, E., Nelson, A., 2008. A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data. ISPRS J. Photogr. Remote Sens., 63(4):399-408.

[7]Han, N., Wang, K., Yu, L., et al., 2012. Integration of texture and landscape features into object-based classification for delineating Torreya using IKONOS imagery. Int. J. Remote Sens., 33(7):2003-2033.

[8]Hermosilla, T., Ruiz, L.A., Recio, J.A., et al., 2012. Assessing contextual descriptive features for plot-based classification of urban areas. Landscape Urban Plan., 106(1):124-137.

[9]Hu, S., Wang, L., 2013. Automated urban land-use classification with remote sensing. Int. J. Remote Sens., 34(3):790-803.

[10]Jacquin, A., Misakova, L., Gay, M., 2008. A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landscape Urban Plan., 84(2):152-165.

[11]Li, G., Weng, Q., 2005. Using Landsat ETM+ imagery to measure population density in Indianapolis, Indiana, USA. Photogr. Eng. Remote Sens., 71(8):947.

[12]Livingston, F., 2005. Implementation of Breiman’s random forest machine learning algorithm. ECE591Q Machine Learning Journal Paper.

[13]Lu, D., Weng, Q., 2006. Use of impervious surface in urban land-use classification. Remote Sens. Environ., 102(1-2):146-160.

[14]Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens., 28(5):823-870.

[15]Mathieu, R., Freeman, C., Aryal, J., 2007. Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery. Landscape Urban Plan., 81(3):179-192.

[16]Novack, T., Esch, T., Kux, H., et al., 2011. Machine learning comparison between WorldView-2 and QuickBird-2-simulated imagery regarding object-based urban land cover classification. Remote Sens., 3(10):2263-2282.

[17]Pacifici, F., Chini, M., Emery, W.J., 2009. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sens. Environ., 113(6):1276-1292.

[18]Ridd, M.K., 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int. J. Remote Sens., 16(12):2165-2185.

[19]Settle, J.J., Drake, N.A., 1993. Linear mixing and the estimation of ground cover proportions. Int. J. Remote Sens., 14(6):1159-1177.

[20]Shaban, M.A., Dikshit, O., 2001. Improvement of classification in urban areas by the use of textural features: the case study of Lucknow City, Uttar Pradesh. Int. J. Remote Sens., 22(4):565-593.

[21]Shahtahmassebi, A., Pan, Y., Lin, L., et al., 2014. Implications of land use policy on impervious surface cover change in Cixi County, Zhejiang Province, China. Cities, 39:21-36.

[22]Shanmugan, K.S., Narayanan, V., Frost, V.S., et al., 1981. Textural features for radar image analysis. IEEE Trans. Geosci. Remote Sens., GE-19(3):153-156.

[23]Smith, M.O., Ustin, S.L., Adams, J.B., et al., 1990. Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sens. Environ., 31(1):1-26.

[24]Trimble Germany GmbH, 2012. eCognition Developer 8.7.1: Reference Book. Documentation, T. Munich, Germany.

[25]Wu, B., Wang, X., Shen, H., et al., 2012. Feature selection based on max-min-associated indices for classification of remotely sensed imagery. Int. J. Remote Sens., 33(17):5492-5512.

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