Full Text:   <384>

Summary:  <387>

CLC number: P235

On-line Access: 2017-12-05

Received: 2017-03-20

Revision Accepted: 2017-08-23

Crosschecked: 2017-11-07

Cited: 0

Clicked: 908

Citations:  Bibtex RefMan EndNote GB/T7714


Cheng-ming Ye


-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2017 Vol.18 No.12 P.984-990


Experimental approach for identifying building surface materials based on hyperspectral remote sensing imagery

Author(s):  Cheng-ming Ye, Peng Cui, Saied Pirasteh, Jonathan Li, Yao Li

Affiliation(s):  Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China; more

Corresponding email(s):   rsgis@sina.com

Key Words:  Building materials, Hyperspectral remote sensing (HRS), Spectral recognition, Spectrum analysis

Cheng-ming Ye, Peng Cui, Saied Pirasteh, Jonathan Li, Yao Li. Experimental approach for identifying building surface materials based on hyperspectral remote sensing imagery[J]. Journal of Zhejiang University Science A, 2017, 18(12): 984-990.

@article{title="Experimental approach for identifying building surface materials based on hyperspectral remote sensing imagery",
author="Cheng-ming Ye, Peng Cui, Saied Pirasteh, Jonathan Li, Yao Li",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Experimental approach for identifying building surface materials based on hyperspectral remote sensing imagery
%A Cheng-ming Ye
%A Peng Cui
%A Saied Pirasteh
%A Jonathan Li
%A Yao Li
%J Journal of Zhejiang University SCIENCE A
%V 18
%N 12
%P 984-990
%@ 1673-565X
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1700149

T1 - Experimental approach for identifying building surface materials based on hyperspectral remote sensing imagery
A1 - Cheng-ming Ye
A1 - Peng Cui
A1 - Saied Pirasteh
A1 - Jonathan Li
A1 - Yao Li
J0 - Journal of Zhejiang University Science A
VL - 18
IS - 12
SP - 984
EP - 990
%@ 1673-565X
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1700149

The management of hazardous building materials poses legal and financial challenges for those in the construction, real estate, and property management fields. Building surface materials have different spectral responses in the electromagnetic energy spectrum. Remote sensors can receive the energy reflection and transmission from such materials. In this study we investigated the spectral characteristics of building materials in wavelengths ranging from 350 nm to 2500 nm. We explored a new method for identifying color steel, clay, glazed tile, and asphalt concrete using hyperspectral remote sensing based on building material spectrum characteristics. We discussed methods for extracting information about the construction materials from hyperspectral remote sensing images. We described a practical applied model, based on spectrum measurements, for the analysis of common building materials, and tested the model using hyperspectral remote sensing data from the EO-1 Hyperion sensor and Chinese airborne hyperspectral data from the pushbroom hyperspectral imager (PHI) spectrometer, covering an urban area. Our results show that building surface materials can be identified from hyperspectral remote sensing images with a reasonable quality, based on the spectral sensitivity of different building materials. For example, concrete and asphalt are more sensitive than other materials. We concluded that the proposed method based on hyperspectral remote sensing images and spectral recognition techniques is an efficient way to extract information about building materials.


方法:1. 设计建筑物材质信息提取流程(图1),并对高光谱数据进行基础处理;2. 对建筑物材料进行光谱测试(波长范围为350~2500 nm,图3),并完成各类建筑物的诊断性光谱分析;3. 利用光谱角度法(公式(1))和光谱信息散度法(公式(2))进行材质信息提取(图5和6);4. 综合分析两种方法的应用过程与控制参数和准确率的关系。
结论:1. 两种方法皆可提取建筑物材质信息,但在应用过程中需要进行参数的适应性调整,这是提高准确率的关键;2. 在建筑物材质信息提取方面,光谱角度法的提取准确率略高于光谱散度法。


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


[1]Bajorski, P., 2011. Statistical inference in PCA for hyperspectral images. IEEE Journal of Selected Topics in Signal Processing, 5(3):438-445.

[2]Chen, Y.S., Lin, Z.H., Zhao, X., et al., 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):2094-2107.

[3]Elnazir, R., Feng, X.Z., Cheng, Z., 2004. Satellite remote sensing for urban growth assessment in Shaoxing City-Zhejiang Province. Journal of Zhejiang University-SCIENCE, 5(9):1095-1101.

[4]Fauvel, M., Tarabalka, Y., Benediktsson, J.A., et al., 2013. Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 101(3):652-675.

[5]Fiumi, L., 2012. Surveying the roofs of Rome. Journal of Cultural Heritage, 13(3):304-313.

[6]Franke, J., Roberts, D.A., Halligan, K., et al., 2009. Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 113(8):1712-1723.

[7]Geng, X.R., Ji, L.Y., Sun, K., 2016. Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data. Frontiers of Information Technology & Electronic Engineering, 17(5):403-412.

[8]Hadigheh, S.M.H., Ranjbar, H., 2013. Lithological mapping in the eastern part of the central Iranian volcanic belt using combined ASTER and IRS data. Journal of the Indian Society of Remote Sensing, 41(4):921-931.

[9]He, C., Zhao, Y.Y., Tian, J., et al., 2013. Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion. International Journal of Remote Sensing, 34(4):1127-1145.

[10]Huang, X., Lu, Q.K., Zhang, L.P., 2014. A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 90:36-48.

[11]Jin, X.Y., Davis, C.H., 2005. Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. EURASIP Journal on Advances in Signal Processing, 2005(14):2196-2206.

[12]Keshava, N., 2004. Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Transactions on Geoscience and Remote Sensing, 42(7):1552-1565.

[13]Kotthaus, S., Smith, T.E.L., Wooster, M.J., et al., 2014. Derivation of an urban materials spectral library through emittance and reflectance spectroscopy. ISPRS Journal of Photogrammetry and Remote Sensing, 94(8):194-212.

[14]Li, Z.Y., Li, J., Zhou, S.L., et al., 2015. Developing an algorithm for local anomaly detection based on spectral space window in hyperspectral image. Earth Science Informatics, 8(4):741-749.

[15]Martin, J.W., 1993. Quantitative characterization of spectral ultraviolet radiation-induced photodegradation in coating systems exposed in the laboratory and the field. Progress in Organic Coatings, 23(1):49-70.

[16]Mitchell, J.J., Glenn, N.F., 2009. Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.). International Journal of Remote Sensing, 30(23):6099-6119.

[17]Onojeghuo, A.O., Blackburn, G.A., 2011. Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats. Remote Sensing of Environment, 115(8):2025-2034.

[18]Petropoulos, G.P., Vadrevu, K.P., Xanthopoulos, G., et al., 2010. A comparison of spectral angle mapper and artificial neural network classifiers combined with landsat TM imagery analysis for obtaining burnt area mapping. Sensors, 10(3):1967-1985.

[19]Ran, Q.H., Qian, Q., Li, W., et al., 2015. Impact of earthquake-induced-landslides on hydrologic response of a steep mountainous catchment: a case study of the Wenchuan earthquake zone. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 16(2):131-142.

[20]Sadezky, A., Muckenhuber, H., Grothe, H., et al., 2005. Raman microspectroscopy of soot and related carbonaceous materials: spectral analysis and structural information. Carbon, 43(8):1731-1742.

[21]Shahtahmassebi, A., Yu, Z.L., Wang, K., et al., 2012. Monitoring rapid urban expansion using a multi-temporal RGB-impervious surface model. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 13(2):146-158.

[22]Tuia, D., Ratle, F., Pacifici, F., et al., 2009. Active learning methods for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 47(7):2218-2232.

[23]Vega, S.H., Huaman, D.L., Manian, V., 2012. Object segmentation in hyperspectral images using active contours and graph cuts. International Journal of Remote Sensing, 33(4):1246-1263.

[24]Vu, T.T., Yamazaki, F., Matsuoka, M., 2009. Multi-scale solution for building extraction from LiDAR and image data. International Journal of Applied Earth Observation and Geoinformation, 11(4):281-289.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE