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Abstract: 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.

基于高光谱遥感影像的建筑物表面材质识别方法

[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.