oalogo2  

AUTHOR(S): 

Seyed Yousef Sadjadi, Saeid Parsian

 

TITLE

Combining Hyperspectral and LiDAR Data for Building Extraction using Machine Learning Technique

pdf PDF

ABSTRACT

In this study, the fusion of hyperspectral and LiDAR data was used to propose a new method to detect buildings using the machine learning algorithm. The data sets provided by the National Science Foundation (NSF) - funded by the Centre for Airborne Laser Mapping (NCALM)- over the University of Houston campus and the neighbouring urban area, were used. The objectives of this study were: 1) automatic buildings extraction using the hyperspectral and LiDAR fused data (automation), 2) detection of the maximum number of listed buildings in the study area (completeness), and 3) achieving high accuracy in building detection throughout the classification procedure (accuracy and precision). After classification of the buildings, a comparison was made between the results obtained by the proposed method and the reference method in this field (Debes et al., 2014). Our proposed method showed a better accuracy for buildings detection in a much shorter time compared to the reference method. The accuracy of the classification was assessed by four parameters of Precision, Completeness, Overall Accuracy and Kappa Coefficient, and the values of 96%, 100%, 99%, and 0.94 were obtained, respectively.

KEYWORDS

Building detection, Hyperspectral, LiDAR, Machine Learning, Decision Tree

REFERENCES

[1] Jensen, J. R., & Cowen, D. C. (1999). Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogrammetric engineering and remote sensing, 65, 611-622.

[2] Gruen, A., Baltsavias, E., & Henricsson, O. (Eds.). (2012). Automatic extraction of man-made objects from aerial and space images (II). Birkhäuser.

[3] Mayer, H. (1999). Automatic object extraction from aerial imagery—a survey focusing on buildings. Computer vision and image understanding, 74(2), 138-149.

[4] Simone, G., Farina, A., Morabito, F. C., Serpico, S. B., & Bruzzone, L. (2002). Image fusion techniques for remote sensing applications. Information fusion, 3(1), 3-15.

[5] Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), 823-854.

[6] Simental, E., Ragsdale, D., Bosch, E., Dodge Jr, R., & Pazak, R. (2003, May). Hyperspectral dimension reduction and elevation data for supervised image classification. In Proceedings, ASPRS Annual Conference, Anchorage, AK-USA.

[7] Esteban, J., Starr, A., Willetts, R., Hannah, P., & Bryanston-Cross, P. (2005). A review of data fusion models and architectures: towards engineering guidelines. Neural Computing & Applications, 14(4), 273-281.

[8] Du, P., Liu, S., Xia, J., & Zhao, Y. (2013). Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 14(1), 19-27.

[9] Dong, J., Zhuang, D., Huang, Y., & Fu, J. (2009). Advances in multi-sensor data fusion: algorithms and applications. Sensors, 9(10), 7771-7784.

[10] Zhang, Y. (2004). Understanding image fusion. Photogrammetric engineering and remote sensing, 70(6), 657-661.

[11] Brill, E., & Wu, J. (1998, August). Classifier combination for improved lexical disambiguation. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics-Volume 1 (pp. 191-195). Association for Computational Linguistics.

[12] Mitchell, T. M. (1997). Machine Learning (International Edition). Computer Science Series. McGraw-Hill, New York.

[13] Polikar, R. (2006). Ensemble based systems in decision making. Circuits and systems magazine, IEEE, 6(3), 21-45.

[14] Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review,33(1-2), 1-39.

[15] Polikar, R. (2006). Ensemble based systems in decision making. Circuits and systems magazine, IEEE, 6(3), 21-45.

[16] Debes, C., Merentitis, A., Heremans, R., Hahn, J., Frangiadakis, N., van Kasteren, T., ... & Philips, W. (2014). Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 7(6), 2405-2418.

[17] Lee, C., & Landgrebe, D. (1993). Feature extraction and classification algorithms for high dimensional data.

[18] Jutten, C., & Herault, J. (1991). Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal processing,24(1), 1- 10.

[19] Boardman, J. W., & Kruse, F. A. (1994). Automated spectral analysis: a geological example using AVIRIS data, north Grapevine Mountains, Nevada. In Proceedings of the Thematic Conference on Geologic Remote Sensing(Vol. 1, pp. I407). Environmental Research Institute of Michigan.

[20] Pitas, I. (2000). Digital image processing algorithms and applications. John Wiley & Sons.

Cite this paper

Seyed Yousef Sadjadi, Saeid Parsian. (2017) Combining Hyperspectral and LiDAR Data for Building Extraction using Machine Learning Technique. International Journal of Computers, 2, 88-93

 

cc.png
Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0