SVM Classification for Sensitive Land Parcels in Highway Route Selecting Based on ALOS Image Combining NDVI

Abstract:

In order to improve the efficiency and cost-saving investigation for sensitive land parcels for road route selecting, this paper demonstrates the methodology of Support Vector Machine (SVM) classification combining with the Normalized Difference Vegetation Index (NDVI) to identify the land parcels using ALOS remote sensing data. One part of the road corridor is taken as the study area in City Group of Changsha, Zhuzhou and Xiangtan, which is regarded as the two society pilot area. The results show that the high effectiveness and applicability of the method in high density vegetation coverage mountainous regions.

• Periodical: Advanced Materials Research (Volumes 518-523)
• Pages: 5623-5626
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.518-523.5623
• Online since: May 2012
• Authors: Guo Qing Sun, Yu Huan Ren, Meng Meng Liu, Zhu Mei Liu, Ya Lan Liu
• Keywords: Highway Route Selecting, NDVI, Sensitive Land Parcel, Supervised Classification, Support Vector Machine (SVM), Training Set

References

[1] Information on www.moc.gov.cn

[2] Xinyi. Gong, Journal of Hunan Administration Institute, Vol. 59（5）:48-51(2009).

[3] Information on http://www.rednet.cn.

[4] C W Hsu, C J UN: IEEE Transaction on Neural Network, Vol.13(2):415-425 (2002)．

[5] L H Su, M J. Chopping, R Albert, J V. Martonchik, P.C Debra: Remote Sensing of Environment, Vol.107（2）, 299-311(2007 ).

[6] J C Luo, C H Zhou, Y Liang:Journal of Remote Sensing, 2002，Vol.6(1)：50-55(In Chinese).

[7] M F Giles, A Mathur: Remote Sensing of Environment, Vol. 93（2）:107-117(2004).