Full Text:   <2138>

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CLC number: P235

On-line Access: 2017-12-05

Received: 2017-03-20

Revision Accepted: 2017-08-23

Crosschecked: 2017-11-07

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Cheng-ming Ye


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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.

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%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
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%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


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